{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Subgraph as state graph sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "var userHomeDir = System.getProperty(\"user.home\");\n",
    "var localRespoUrl = \"file://\" + userHomeDir + \"/.m2/repository/\";\n",
    "var langchain4jVersion = \"0.36.2\";\n",
    "var langgraph4jVersion = \"1.4-SNAPSHOT\";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash \n",
    "rm -rf \\{userHomeDir}/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/org/bsc/langgraph4j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0mRepository \u001b[1m\u001b[32mlocal\u001b[0m url: \u001b[1m\u001b[32mfile:///Users/bsorrentino/.m2/repository/\u001b[0m added.\n",
      "\u001b[0mAdding dependency \u001b[0m\u001b[1m\u001b[32morg.slf4j:slf4j-jdk14:2.0.9\n",
      "\u001b[0mAdding dependency \u001b[0m\u001b[1m\u001b[32morg.bsc.langgraph4j:langgraph4j-core:1.4-SNAPSHOT\n",
      "\u001b[0mAdding dependency \u001b[0m\u001b[1m\u001b[32morg.bsc.langgraph4j:langgraph4j-langchain4j:1.4-SNAPSHOT\n",
      "\u001b[0mAdding dependency \u001b[0m\u001b[1m\u001b[32mdev.langchain4j:langchain4j:0.36.2\n",
      "\u001b[0mAdding dependency \u001b[0m\u001b[1m\u001b[32mdev.langchain4j:langchain4j-open-ai:0.36.2\n",
      "\u001b[0mAdding dependency \u001b[0m\u001b[1m\u001b[32mnet.sourceforge.plantuml:plantuml-mit:1.2024.8\n",
      "\u001b[0mSolving dependencies\n",
      "Resolved artifacts count: 27\n",
      "Add to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/org/slf4j/slf4j-jdk14/2.0.9/slf4j-jdk14-2.0.9.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/org/slf4j/slf4j-api/2.0.9/slf4j-api-2.0.9.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/org/bsc/langgraph4j/langgraph4j-core/1.4-SNAPSHOT/langgraph4j-core-1.4-SNAPSHOT.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/org/bsc/async/async-generator/3.0.0/async-generator-3.0.0.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/org/bsc/langgraph4j/langgraph4j-langchain4j/1.4-SNAPSHOT/langgraph4j-langchain4j-1.4-SNAPSHOT.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/dev/langchain4j/langchain4j/0.36.2/langchain4j-0.36.2.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/dev/langchain4j/langchain4j-core/0.36.2/langchain4j-core-0.36.2.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/com/google/code/gson/gson/2.10.1/gson-2.10.1.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/org/apache/opennlp/opennlp-tools/1.9.4/opennlp-tools-1.9.4.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/dev/langchain4j/langchain4j-open-ai/0.36.2/langchain4j-open-ai-0.36.2.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/dev/ai4j/openai4j/0.23.0/openai4j-0.23.0.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/com/squareup/retrofit2/retrofit/2.9.0/retrofit-2.9.0.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/com/squareup/retrofit2/converter-jackson/2.9.0/converter-jackson-2.9.0.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/com/fasterxml/jackson/core/jackson-databind/2.17.2/jackson-databind-2.17.2.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/com/fasterxml/jackson/core/jackson-annotations/2.17.2/jackson-annotations-2.17.2.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/com/fasterxml/jackson/core/jackson-core/2.17.2/jackson-core-2.17.2.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/com/squareup/okhttp3/okhttp/4.12.0/okhttp-4.12.0.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/com/squareup/okio/okio/3.6.0/okio-3.6.0.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/com/squareup/okio/okio-jvm/3.6.0/okio-jvm-3.6.0.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/org/jetbrains/kotlin/kotlin-stdlib-common/1.9.10/kotlin-stdlib-common-1.9.10.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/com/squareup/okhttp3/okhttp-sse/4.12.0/okhttp-sse-4.12.0.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/org/jetbrains/kotlin/kotlin-stdlib-jdk8/1.9.25/kotlin-stdlib-jdk8-1.9.25.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/org/jetbrains/kotlin/kotlin-stdlib/1.9.25/kotlin-stdlib-1.9.25.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/org/jetbrains/annotations/13.0/annotations-13.0.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/org/jetbrains/kotlin/kotlin-stdlib-jdk7/1.9.25/kotlin-stdlib-jdk7-1.9.25.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/com/knuddels/jtokkit/1.1.0/jtokkit-1.1.0.jar\u001b[0m\n",
      "\u001b[0mAdd to classpath: \u001b[0m\u001b[32m/Users/bsorrentino/Library/Jupyter/kernels/rapaio-jupyter-kernel/mima_cache/net/sourceforge/plantuml/plantuml-mit/1.2024.8/plantuml-mit-1.2024.8.jar\u001b[0m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "%dependency /add-repo local \\{localRespoUrl} release|never snapshot|always\n",
    "// %dependency /list-repos\n",
    "%dependency /add org.slf4j:slf4j-jdk14:2.0.9\n",
    "%dependency /add org.bsc.langgraph4j:langgraph4j-core:\\{langgraph4jVersion}\n",
    "%dependency /add org.bsc.langgraph4j:langgraph4j-langchain4j:\\{langgraph4jVersion}\n",
    "%dependency /add dev.langchain4j:langchain4j:\\{langchain4jVersion}\n",
    "%dependency /add dev.langchain4j:langchain4j-open-ai:\\{langchain4jVersion}\n",
    "%dependency /add net.sourceforge.plantuml:plantuml-mit:1.2024.8\n",
    "\n",
    "%dependency /resolve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**utility to render graph respresentation in PlantUML**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import net.sourceforge.plantuml.SourceStringReader;\n",
    "import net.sourceforge.plantuml.FileFormatOption;\n",
    "import net.sourceforge.plantuml.FileFormat;\n",
    "import org.bsc.langgraph4j.GraphRepresentation;\n",
    "\n",
    "void displayDiagram( GraphRepresentation representation ) throws IOException { \n",
    "    \n",
    "    var reader = new SourceStringReader(representation.getContent());\n",
    "\n",
    "    try(var imageOutStream = new java.io.ByteArrayOutputStream()) {\n",
    "\n",
    "        var description = reader.outputImage( imageOutStream, 0, new FileFormatOption(FileFormat.PNG));\n",
    "\n",
    "        var imageInStream = new java.io.ByteArrayInputStream(  imageOutStream.toByteArray() );\n",
    "\n",
    "        var image = javax.imageio.ImageIO.read( imageInStream );\n",
    "\n",
    "        display(  image );\n",
    "\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Graph State**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import org.bsc.langgraph4j.prebuilt.MessagesState;\n",
    "import org.bsc.langgraph4j.state.Channel;\n",
    "import org.bsc.langgraph4j.state.AppenderChannel;\n",
    "\n",
    "public class State extends MessagesState<String> {\n",
    "\n",
    "    public State(Map<String, Object> initData) {\n",
    "        super( initData  );\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Utility action to simplify node creation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import org.bsc.langgraph4j.action.AsyncNodeAction;\n",
    "\n",
    "AsyncNodeAction<State> makeNode( String id ) {\n",
    "    return node_async(state ->\n",
    "            Map.of(\"messages\", id)\n",
    "    );\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a subgraph as state graph**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import org.bsc.langgraph4j.StateGraph;\n",
    "import org.bsc.langgraph4j.action.AsyncNodeAction;\n",
    "import static org.bsc.langgraph4j.action.AsyncNodeAction.node_async;\n",
    "import static org.bsc.langgraph4j.action.AsyncEdgeAction.edge_async;\n",
    "import static org.bsc.langgraph4j.StateGraph.END;\n",
    "import static org.bsc.langgraph4j.StateGraph.START;\n",
    "\n",
    "var workflowChild = new StateGraph<>(State.SCHEMA, State::new)        \n",
    "                    .addNode(\"child:step_1\", makeNode(\"child:step1\") )\n",
    "                    .addNode(\"child:step_2\", makeNode(\"child:step2\"))\n",
    "                    .addNode(\"child:step_3\", makeNode(\"child:step3\"))\n",
    "                    .addEdge(START, \"child:step_1\")\n",
    "                    .addEdge(\"child:step_1\", \"child:step_2\")\n",
    "                    .addConditionalEdges(  \"child:step_2\",\n",
    "                                edge_async(state -> \"continue\"),\n",
    "                                Map.of( END, END, \"continue\", \"child:step_3\") )\n",
    "                    .addEdge(\"child:step_3\", END)\n",
    "                    ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create graph with a sub graph as state graph**\n",
    "> The subgraph will be merged to the parent and executed as part of it, sharing its state and also `CompileConfig`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NodeOutput{node=__START__, state={messages=[]}}\n",
      "NodeOutput{node=step_1, state={messages=[step1]}}\n",
      "NodeOutput{node=step_2, state={messages=[step1, step2]}}\n",
      "NodeOutput{node=(subgraph)child:step_1, state={messages=[step1, step2, child:step1]}}\n",
      "NodeOutput{node=(subgraph)child:step_2, state={messages=[step1, step2, child:step1, child:step2]}}\n",
      "NodeOutput{node=(subgraph)child:step_3, state={messages=[step1, step2, child:step1, child:step2, child:step3]}}\n",
      "NodeOutput{node=step_3, state={messages=[step1, step2, child:step1, child:step2, child:step3, step3]}}\n",
      "NodeOutput{node=__END__, state={messages=[step1, step2, child:step1, child:step2, child:step3, step3]}}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "var workflow = new StateGraph<>(State.SCHEMA, State::new)        \n",
    "                    .addNode(\"step_1\",  makeNode(\"step1\"))\n",
    "                    .addNode(\"step_2\",  makeNode(\"step2\"))\n",
    "                    .addNode(\"step_3\",  makeNode(\"step3\"))\n",
    "                    .addSubgraph( \"subgraph\", workflowChild )\n",
    "                    .addEdge(START, \"step_1\")\n",
    "                    .addEdge(\"step_1\", \"step_2\")\n",
    "                    .addEdge(\"step_2\", \"subgraph\")\n",
    "                    .addEdge(\"subgraph\", \"step_3\")\n",
    "                    .addEdge(\"step_3\", END)\n",
    "                    ;\n",
    "var compiledWorkflow =  workflow.compile();\n",
    "\n",
    "for( var step : compiledWorkflow.stream( Map.of() )) {\n",
    "    System.out.println( step );\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Display StateGraph** as you defined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var representation = workflow.getGraph( GraphRepresentation.Type.PLANTUML, \"sub graph\", false );\n",
    "\n",
    "displayDiagram( representation );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Display Compiled Graph** result as merged process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nmmH5u+++e2oscaQNiWwTE8c4JE7Dhg2LFSuGmHBwcNiyZYsov3TpUrNmzVCITBEl/fr1Q+KUKFECCfXZZ5+hys/Pr1OnTh988MF/d2dsQyIbxMQx5969e0b/+NSVK1fU72EhccTIBTmFZDH1UcCnOTYksjVMHB1o+ZYDET1l4uiCiUOkERNHB0wcIo2YODpg4hBpxMTRAROHSCMmjg6YOEQaMXF0wMQh0oiJowMmDpFGTBwdMHGINGLi6ICJQ6QRE0cHTBwijZg4OmDiEGnExNEBE4dIIyaODpg4RBoxcXTAxCHSiImjAyYOkUZMHB0wcYg0YuLogIlDpBETRwdMHCKNmDg6YOIQacTE0QETh0gjJo4OmDhEGjFxdMDEIdKIiaMDJg6RRkwcHTBxiDRi4uiAiUOkERNHB0wcIo2YODpg4hBpxMTRAROHSCMmjg6YOEQaMXF0wMQh0oiJowMmDpFGTBwdMHGINGLi6ICJQ6QRE0cHTBwijZg4OmDiEGnExNEBE4dIIyaODpg4RBoxcXTAxCHSiImjAyYOkUZMHB0wcYg0YuLogIlDpBETRwdMHCKNmDg6YOIQacTE0QETh0gjJo4OmDhEGjFxdMDEIdKIiZMXycnJ6lUpcaRaIlIwcfJi8eLFkZGRyqo6cVCOWmWViNSYOHmRkZHRqFGj4OBgsaokDuLm7bffRu1/mxKRChMnj0aNGmVvb7969eqn2YmDuEFJ//795aZElI2Jk0dJSUk1DBA6SBzETe3atbGAcrkpEWVj4uRd37593333XYQOgqZOnTr4t2PHjnIjIlJh4uRdRETE+++/j6ARoYMp1f79++VGRKTCxMmXDh06tG7durqBg4ODXE1Ez2Li5EtQUJCTkxPipmbNmv7+/nI1ET2LiZMvDx48cHR0bNCgQZ06dbAsVxPRs5g4+TVnzpzXX3998uTJcgUR5cDE0eThw4eJiYkHDhxYv349IsbLwNPA1dW1du3a+FesAqpmz54dGBi4f//+c+fO/fnnn/LuiGwVE8e4W7du7d69G8GBBJkxYwZCBFkTERFx9uzZjIyMrGchiaQSbI6Whw4dCgkJ8fb29vDwEDvZsWPHtWvX5AcjshlMnP/6/fffd+3ahWhwd3f38/OLjIzMGS75kZmZGR0dvXz58unTp+MhEGFpaWnyQRBZNSbO03v37gUHB7u5uWFEc/jwYTknCszp06cXLlw4bdo0/JuamiofFpE1sunEOXbsGIJm5syZJ0+elPOgEF28eHHx4sWInqCgIL7hRdbNFhPn8ePHmzdvdnZ2Dg0NxUxHDoC/DhIQEy4MtTjbImtlc4mzZ8+eiRMnFubsKbeuXLmC0Jk1a9atW7fkoyeycDaUOAkJCRMmTNi9e7fcxYuky5cve3p6Llq0iG+ukzWxicRBp/X19V2wYMHt27flnl20nT59ety4cdHR0fIpEVkm60+c1NTU0aNHx8bGyr25YGRmZiLdzp49K1fkQ0BAwJw5cx49eiSfG5GlsfLEiYiImDJlys2bN+VObFZYWFjTpk137NihlAwePNjHx0fVxKRjx47Z2dlhVCJX5M+JEyfGjh1748YN+QyJLIo1J87WrVuXLFki910NXFxckBpOTk5KSbVq1datW6dqYtLSpUvLlClTEG+BpaWljR8//vz58/J5ElkOq02c0NBQTEbkXqtN165dy5cvj9CJjIzE6rlz57CsTJQQPcOGDRsxYsTevXtFyZ07dxA0AwcOdHd3Hzp0aKtWrZTy5cuXo9zZ2Tk9PV0U5sft27exq7i4OPlsiSyEdSbOgQMH0NXl/mra7Nmzp02bdvjw4U8++QSr9vb2c+fOLVu2bN++fbEaFBRUuXJl0fKbb76pUqXKyJEj27VrV6JEiePHj6NwwIABGAT98MMPjRs3RjYNHz5cNEZ5kyZNMDCpWrWqp6cnSvz9/ceNG4cI69ixY27negJSDKFz+fJl+ZyJLIEVJs6vv/6K+JB7qmkTJkxo2bJlcHAwgubgwYMXLlxAapw8eRKjmJIlSyYkJCAjOnXqhJYxMTFIGTHwQc8vVqwYBjUIneLFix87dizL8AXOUqVKLV68GMtohukVwgUte/XqNXr06CzDcAmjJwyO2rZti4RSH4Z2GRkZY8aMwYJ85kRFnrUlzuPHj9G30fPlbmrCqFGjMDAJCQnBiCY8PBwlmzZtQiggJuLj4xElyIXOnTuLvOjevTuSQmyYmZmJcHFzc/vyyy+VQnHb+OjRo1j+/PPP7VS+/fZb0Qa7ql+/PqINgSXCKw9SUlJcXFzkkycq8qwtcQICAkSH12jdunWVKlXC4AVzpbVr12YZbht/9NFHorZHjx4VKlSoWLGiuG2MbOrXr5+owlSrdOnSiYmJLVq0GDJkiChU3zZ2dHQUkzK1qKiomjVrYsaHSdmgQYPyc4N58+bNu3fvls+fqGizqsT57bffpk6dKnfN5/nqq6++/vrrwMBAzKrS0tK6du36/fffiyqMRMQIRdw2dnJyat68eXJyMsYmDg4OGB+hsEuXLu+9996JEyf8/f3LlSun3Dbu1q1brVq10BLDJcy8MOxCrtWpUwdJ0b59+7Fjx2Y/ft5NmjSJn0gmy2JViYMhxpkzZ+R++TypqamHDh3Cws6dO/EvYkL9JlebNm2U28YIoOrVq5cw6NOnz9WrV1Ho5+eHyRemSJh8IYaU28bIoAYNGiCtEEMIGiTOxYsXRQCJB8q/6OhozAflZ4GoCLOqxJk8ebLcKfWGvEhISJDeZrp27VpKSoq6RJGUlHT9+nW5VD+8m0OWxXoSB5OdlStXyj3S2i1btgxDLfm5ICqqrCdx/P39ExMT5R5p7eLj49etWyc/F0RFlfUkzowZM+TuaBu8vb3l54KoqGLiFLb09PSkpCS5NB+YOGRBmDiFZ/To0a1bt37ppZeUTwPqwsvLS34uiIoqJo5s0aJF4oM2uqtatWrv3r2LFy/OxCGbZT2JI74qmQd37txRr3bt2rVhw4bqkixDG6mZmpkqNdGsbNmy+iYOZ1VkQawncVavXp2QkCB3R7MiIyM/++yzMmXKvPLKK+KzPOPGjUMilCxZskGDBhiPZBl+lWbQoEFoUKFChREjRii/WzpgwIBevXr5+PjUrVsXe+jZs6f4QOBz6Zs48fHxQUFB8nNBVFRZT+JcuHDB399f7pFmtWzZEsmCHuvn57d+/XqU7N2718HBoUaNGijZsGEDSvr374+4wQBq7NixxYoVmzt3rti2Xbt2pUuXfu2113788ce2bdva2dmNHz9evXNT9E2cZcuWXb9+XX4uiIoq60kccHFxkXukWdWqVfvwww9v3LihLlTPqpKSkpAy3333nVht3LhxmzZtxDISp3r16srPdDk6OrZo0UIsm6dv4vAzx2RZrCpxMLE6deqU3ClNc3Z2xtgE4xQvLy/la9zqxNmxYwcaIFkaGyAsateuLaqQOBgiiWXAzKt48eJXrlxRSkzRMXF+/vnnbdu2yc8CURFmVYnzxx9/5ParVatWrWratCli5YsvvhAl6sRBfxZVPtkwixFVUuIMHjwYLU19u0pNx8SZOHEi/8ADWRarShzYvHnznj175K5pFkY3vXr1wgjl0qVLWYbEUQYyiYmJL7zwQufOnZ/ZwEBKnHfeeefVV19V1ZukV+KsWbOGf8eKLI61JQ7glT81NVXuoDncuXNnyJAhwcHBp0+f7tixY5UqVcSXvF1dXTFaCQ0NvXbtGlb79u0rfgkwKipq37594hdFswyJ8+KLL6JxbGwsHrFEiRLidwLNOHLkCOZBpUqVwqAJC+fOnZNbaBYfHz9r1iz5zImKPCtMHETJ2LFjn/vDo2j29ttvFytWDPnSuHHjwMBAUY7O7ODggEJkSpbhlyj69++PQEEJIqZDhw6iGWpr1arVrFkzlGMc1LNnT+kOdE4lS5YUv+8lKL8cmFsYi40fP/7hw4fymRMVeVaYOE8NP67u4uKi5Tc9ERNGP8WDAYg6QW7evHnq1Cn1z+IosypsruWGsV7wWGPGjPn999/lcyayBNaZOIDImDRp0nNHOnkm3ccRUlJSmhij5Y6yFomJieLnSuWzJbIQVps4gNnHuHHj9OrtEvT8kSNHSoUYBC0zJm9/mkoSERGBgduDBw/k8ySyHNacOE8Nv7Xu7OyMvip3X4ty+/bt+fPnr1mzRj49Iktj5YkjoK96e3unpaXJXdkSHDlyZPz48QkJCfJZEVkgm0gcuH79OqYkGzdulDt0ESb+uGhgYOCTJ0/k8yGyTLaSOMLhw4cnTpy4bds2uXMXMUlJSR4eHphJZfEmMVkX20ocISIiwtXVdcWKFUVwnhUeHo5jW7hw4d27d+XjJrJ8tpg4QnJyspeXl7u7u/hz43+ts2fPYkQzZcqUsLAwflWKrJjtJo7w559/7tmzZ/r06Yge9PbCHPVkZmb+/PPPs2fPnjZt2qpVq27evCkfHJHVsfXEUSB6jhw5gv6P9Jk5c+bWrVsvX74sh0S+paamIuDmzp3r5uY2Y8aMXbt2ZfFODdkSJo4R//73v48dO7ZkyRJ3A2TQggULtm3bduLECe1/+AWztpMnT27fvn3hwoXTDbArPz+/iIgIjG7khySyDUyc53vy5ElGRgYyKDQ0NCAgYIaBRzaRSqCUABr4+/tv2LDh6NGjmKk9fvxY3imRTWLi6KB69epyEREZw8TRAROHSCMmjg6YOEQaMXF0wMQh0oiJowMmDpFGTBwdMHGINGLi6ICJQ6QRE0cHTBwijZg4OmDiEGnExNEBE4dIIyaODpg4RBoxcXTAxCHSiImjAyYOkUZMHB0wcYg0YuLogIlDpBETRwdMHCKNmDg6YOIQacTE0QETh0gjJo4OmDhEGjFxdMDEIdKIiaMDJg6RRkwcHTBxiDRi4uiAiUOkERNHB0wcIo2YODpg4hBpxMTRAROHSCMmjg6YOEQaMXF0wMQh0oiJowMmDpFGTBwdMHGINGLi6ICJQ6QRE0cHTBwijZg4OmDiEGnExNEBE4dIIyaODpg4RBoxcXTAxCHSiImjAyYOkUZMHB0wcYg0YuLogIlDpBETRwdMHCKNmDg6YOIQacTE0QETh0gjJo4OmDhEGjFxdMDEIdKIiaMDJg6RRkwcHTBxiDRi4uiAiUOkERNHB0wcIo2YODpg4hBpxMTJi+TkZPWqlDhSLREpmDh5sXjx4sjISGVVnTgoR62ySkRqTJy8yMjIaNSo0eHDh8WqkjiIG5Sj9r9NiUiFiZNHo0aNqlWrlggdkTiImzfffPPvf/+73JSIsjFx8igpKalGjRr29vYIHSQO4qZevXpYOHHihNyUiLIxcfKuT58+GNTUrl0bQdOwYcO6deu+9957ciMiUmHi5F1ERISDgwPiBtMrLGDIExISIjciIhUmTr60bdtWjHGgfv36cjURPYuJky9BQUGYSYnEcXd3l6uJ6FlMnHx58OBBkyZNXnvtNXt7eyzL1UT0LCZOfs2ZMweJ880338gVRJQDE0eThw8fJiYmhoeHBwcHz5s3b5bKtGnTatWqNXXqVHUh2qDlgQMHEhIS/vzzT3l3RLaKiWPcrVu39uzZM3fu3JkGs2fP3rhxY2RkZFJS0p07d37//fe7d+/+9ttvWQZIFrEAdw3QBgmF9ps2bcIgyNvbGztBEu3atevatWvygxHZDCbOfyFHwsLCvLy8PD09lyxZcuzYsczMTCVW8g8xdPz48ZUrV86YMQOPsmHDhvT0dPkgiKwaE+fpvXv3QkJCPDw85s+fHx0djRGKHBUFIz4+funSpXhcpNvVq1flwyKyRjadOBhxoMNj6hQXF6fjWCa3Ll++vHz5chwJgo9veJF1s8XEefz48datW6dMmYJ/MdORA+Cvc+LECUzo5s2bx9kWWSubS5y9e/dOnjz56NGjcncvMlJTUxE6GHndvn1bPnoiC2dDiZOYmOjs7Lxv3z65ixdJKSkps2bNWrp0Kd9cJ2tiE4mDTrtw4cJFixZh1CD37KItLi5u4sSJx44dk0+JyDJZf+JcvXp13Lhx8fHxcm+2HKtXr8Y869GjR/K5EVkaK0+cn3/+2c3N7ebNm3InLjCZmZm+vr5nz56VK/Ln1KlTEyZMSEtLk8+QyKJYc+Ls2LFj+fLlct/VICwsrGnTpthcKRk8eLCPj4+qiUmYAdnZ2UVHR8sV+Ya4mTRp0q+//iqfJ5HlsNrE2bx5MyYjcq/VxsXFBanh5OSklFSrVm3dunWqJiYtXbq0TJkyGOnIFXq4ffu2q6vrmTNn5LMlshDWmTgRERH+/v5yf9Wsa9eu5cuXR+hERkZi9dy5c1hWJkqInmHDho0YMWLv3r2i5M6dOwiagQMHuru7Dx06tFWrVko5Blkod3Z2Tk9PF4X5hH0idFJSUuRzJrIEVpg4Fy9enD59utxTzZo9e/a0adMOHz78ySefYNXe3n7u3Llly5bt27cvVoOCgipXrixafvPNN1WqVBk5cmS7du1KlChx/PhxFA4YMACDoB9++KFx48bIpuHDh4vGKG/SpMn48eOrVq3q6emJEuTguHHjEGEdO3bM892ljIwM7AQL8pkTFXnWljiPHz8eO3bsrVu35G5q2oQJE1q2bBkcHIygOXjw4IULF5AaJ0+exCimZMmSCQkJ6N6dOnVCy5iYGKSMGPhgrFGsWDEMahA6xYsXP3bsGArxuKVKlVq8eDGW0QzTK4QLWvbq1Wv06NFZhuESRk8YHLVt2xYJpT6MXLly5cqUKVPkkycq8qwtcVavXp2rzxOPGjUKA5OQkBCMaMLDw1GyadMmhAJiIj4+HlGCXOjcubPIi+7duyMpxIaZmZkIFzc3ty+//FIpFLeNxQF8/vnndirffvutaINd1a9fH9GGwBLhlTdbt27ds2ePfP5ERZtVJc7du3cxOZK7plnr1q2rVKkSBi+YK61duzbLcNv4o48+ErU9evSoUKFCxYoVxW1jZFO/fv1EFaZapUuXTkxMbNGixZAhQ0Sh+raxo6OjmJSpRUVF1axZc/ny5ZiUDRo0KJ83mCdPnsxPJJNlsarEQU8+c+aM3C+f56uvvvr6668DAwMxq0pLS+vatev3338vqjASESMUcdvYycmpefPmycnJGJs4ODhgfITCLl26vPfeeydOnPD39y9Xrpxy27hbt261atVCS/GzOJhwIdfq1KmzefPm9u3bY+qX/fh5hx1u2LBBfhaIijCrShwMT+ROqUFqauqhQ4ewsHPnTvyLmAgICFBq27Rpo9w2RgBVr169hEGfPn2uXr2KQj8/P0y+MEXC5AsxpNw2RgY1aNAAaYUYQtAgcS5evCgCSDyQLng3hyyL9STO+fPnV65cKfdIvSEvEhISpLeZrl27lpKSoi5RJCUlXb9+XS7VD4Z1/DUvsiDWkziIm8TERLlHWjvMIjEflJ8LoqLKehJHfODFBnl7e8vPBVFRZT2JM2PGDLkv2gYmDlkQJk6hwrwvOjo6LS1NrsgHLy8v+bkgKqqYOIVk6tSp9vb24r32ChUqrFixQm6RVxzjkAVh4sgWLVokPmijr2HDhs2dO/fSpUvbt2+vXbt2uXLlTL29lVtMHLIg1pM4/v7+eXuvSvpzDl27dm3YsKG6JMvQxsxffTBTZdSECRMw0gkLC5Mrci8+Pn7dunXyc0FUVFlP4iQnJ+f28ziRkZGfffZZmTJlXnnllcmTJ6Nk3LhxZcuWLVmyZIMGDXr37p1l+B2sQYMGoQGmQiNGjFB+KXnAgAG9evXy8fGpW7cu9tCzZ0/xgUAtMNhB4ig/dpEfy5Yt4+dxyIJYT+KASA3tWrZsiWQJCgry8/Nbv349SpACDg4ONWrUQMmGDRtQ0r9/f8SNp6fn2LFjixUrhrAQ27Zr16506dKvvfbajz/+2LZtWyTI+PHj1Ts3JTMzs02bNi+//HKuvuBuiqurq/wsEBVhVpU4eMHP1feqqlWr9uGHH964cUNdqJ5VJSUlIWW+++47sdq4cWOEhVhG4lSvXl35mS5HR8cWLVqIZfNGjRpVokQJEXD5FB0dHRISIj8LREWYVSXOb7/9NnXqVLlfmubs7IyxCcYpXl5eyte41YmzY8cONECyNDbAhKt27dqiComDIZJYBsy8ihcvfuXKFaXEqIkTJ6KZ+AGd/Js0aRK/O06WxaoSBwICAnL1+zirVq1q2rQpYuWLL74QJerE2bZtm6jyyYZhlKiSEmfw4MFoaf7tpxkzZrzwwgtLly6VK/Jky5Ytu3fvls+fqGiztsR5/Pjx6NGjc3WLBKObXr16Yehx6dKlLEPiKAOZxMREZETnzp2f2cBASpx33nnn1VdfVdXLdu7ciV1pvNfzXIg2FxcX+eSJijxrSxz49ddftfwu1507d4YMGRIcHHz69OmOHTtWqVJFfMnb1dUVo5XQ0NBr165htW/fvuKXAKOiovbt26dMiJA4L774IhrHxsZirlSiRAnxO4GmODo6Yrfe3t6LsiUlJcmNtMnIyBgzZkwWf+eYLJAVJg6Eh4c/9y9VIXHefvvtYsWKIQgaN24cGBgoyuPj4x0cHFCITMky/BJF//79ESgoQcR06NBBNENtrVq1mjVrhnIMXnr27CndgZZUrlz5/z5urIIpm9xIAxz25MmTL1++LJ8zkSWwzsQBDFLUP6xlCmIiISFBLjX8BLo6QW7evHnq1Cn1z+Iosyps/twbxnq5ffs24gaZKJ8tkYWw2sQBDCKWLFki91qdSPdxhJSUlCbGmL+jrFFaWtr48ePPnz8vnyeR5bDmxIFDhw5NmTIlz38ZyoyxY8eOHDlSKsQDLTMm/wdw4sQJPCKGXfIZElkUK08cSE1NHT16dGxsrNyJLQemh3PmzHn06JF8bkSWxvoTB/78809fX98FCxYo34qyFKdPn8ZMKjo6Wj4lIstkE4kjJCQkTJw4cffu3XK3LpIuX77s5eW1aNEifqqYrIkNJY6wZ88e5M7hw4flLl5kXLlyZbYBRmTy0RNZOJtLnKeGzyVv2bLF2dl548aN+fyrmPo6duzY9OnTkTVpaWnyQRNZBVtMHAV6uJub28yZM0+ePCn3/kJ08eLFxYsX40iCgoIePHggHyWRFbHpxBHu3bsXHByMDo/BRWHOtk6fPr1w4UI8Lv7lr2qRjWDi/Nfvv/++a9euGTNmuLu7IwUiIyMzMjLknMgHzOCio6OXL1+OqZOHhwdiLj09XT4IIqvGxDHu1q1bu3fvxqjH09MTGeTt7b1+/fqIiIizZ89qiSFsjpaHDh0KCQnBrA35gp14eXnt3Lnz2rVr8oMR2QwmjiYPHz5MTEwMDw9H7syZMwcB5GXgaVC9enWxIAoBbdBy//79CQkJfHubSMHE0QESRy4iImOYODpg4hBpxMTRAROHSCMmjg6YOEQaMXF0wMQh0oiJowMmDpFGTBwdMHGINGLi6ICJQ6QRE0cHTBwijZg4OmDiEGnExNEBE4dIIyaODpg4RBoxcXTAxCHSiImjAyYOkUZMHB0wcYg0YuLogIlDpBETRwdMHCKNmDg6YOIQacTE0QETh0gjJo4OmDhEGjFxdMDEIdKIiaMDJg6RRkwcHTBxiDRi4uiAiUOkERNHB0wcIo2YODpg4hBpxMTRAROHSCMmjg6YOEQaMXF0wMQh0oiJowMmDpFGTBwdMHGINGLi6ICJQ6QRE0cHTBwijZg4OmDiEGnExNEBE4dIIyaODpg4RBoxcXTAxCHSiImjAyYOkUZMHB0wcYg0YuLogIlDpBETRwdMHCKNmDg6YOIQacTE0QETh0gjJo4OmDhEGjFxdMDEIdKIiaMDJg6RRkwcHTBxiDRi4uiAiUOkERNHB0wcIo2YOHmRnJysXpUSR6olIgUTJy8WL14cGRmprKoTB+WoVVaJSI2JkxcZGRmNGjXy9/cXq0riHDly5K233kLtf5sSkQoTJ49GjRplb2/v6+v7NDtxEDevv/76oEGD5KZElI2Jk0dJSUk1DBA6SBzETa1atbCAcrkpEWVj4uRd375933//fYQOguaNN97Avx9//LHciIhUmDh5FxER0bp16+rZMMY5ePCg3IiIVJg4+dKhQwcnJyeROO+8845cTUTPYuLkS1BQEGZSiBt7e/tVq1bJ1UT0LCZOvjx48MDRoG7duliWq4noWUyc/JozZ87rr78+bdo0uYKIcrDpxLly5UpKSkpMTMyRI0d++umn9dn8/Py8VbyM8czm6ur6xhtv4F+lRG6aTdmhr6+v8lh79+7Fo0dHR6cYPHnyRD5KIitihYmTmpp69uxZdOMNGzasWLFCiQxkwYxnrTIICwvbt2/foUOHzma7ceNGVm4cOHBALjIrLS1NeazDhw/j0XEM4mCkI1QiDGexePFiJBTOKy4uDtn0+PFj+cyJijyLTBx02oSEhIMHDwYGBopAUaeJv79/aGgounFsbOylS5fk7m6xMCJDQuG8Nm7cKLLJw8NDpJKI1JUrV+7ZswdnnZ6eLj9lREVDUU8cvJhHRESgg6kzBbOS4ODg8PBw9EC5X9qw5ORkjICQRxgNiTASeYTV3bt3I6Pv378vP79EhasIJc6DBw/i4+M3b94swgV9xt3dPSAgYP/+/UlJSXL3Is0wOEISYUY2a9Ys8aziGcZ8MzIy8vbt2/J/A1FB+isTJy0tbdeuXegGIl8wL8Bs6MSJE3fu3JE7jSXIzMzE4MtShl0XLlzAk79gwQIE0PTp05FBq1evRuI/fPhQ/n8i0k+hJg6iBAOWefPmIWJwlWO0HxUVlZGRIfeGv05YWFjTpk137NihlAwePNjHx0fVxKRjx47Z2dlFR0fLFRYC0y4kPqJfBFBgYCDSk++dkb4KPHFSUlKCg4OnTZvm5uaGIUB4eDiGNvLFXmS4uLggNZycnJSSatWqrVu3TtXEpKVLl5YpUwYjHbnCMmG8s2HDBjEF8/Pzi4mJ+fPPP+X/XaJcKpDEwfW6efNmXKyurq4LFy7Ei798ORdVXbt2LV++PEInMjISq+fOncOyMlFC9AwbNmzEiBF79+4VJRi1IWgGDhyIkx06dGirVq2U8uXLl6Pc2dk5PT1dFFouvEjs27dvzpw5mPlixIf0kf/LibTRM3F+/fXXuXPnImVmzZqFi1K+bIuk2bNnY/x1+PDhTz75BKv29vY4hbJly/bt2xerQUFBlStXFi2/+eabKlWqjBw5sl27diVKlDh+/DgKBwwYgEHQDz/80LhxY2TT8OHDRWOUN2nSZPz48VWrVsUYwd/ff9y4ccivjh073rx58z+PbZmQPtu3b0f04KnD9PPevXvydUBkmg6JI4Jm6tSp6Ffnz5+Xr9AibMKECS1btsSkD0Fz8ODBCxcuIDVOnjyJUUzJkiUTEhIQE506dUJLBChSRgx8MH4pVqwYBjUIneLFi4sR3K1bt0qVKrV48WIsoxmmV8gXtOzVq9fo0aOxjKETRkZt27ZFPD1zEJbs1KlT8+fP9/DwWLt27d27d+UrgyiHvCcOLjhMHNBp8e/Vq1fli7HIGzVqFAYmISEhGNGEh4ejZNOmTcgFxER8fDyiBNHQuXNn5AWqunfvjrAQG2ZmZiJc3NzcvvzyS6VQ3DY+evQolj///HM7lW+//RaF2E/9+vWRa0grkVzWBJHq6+uLadf+/fsfPXokXytE2fKSOOiQeIWfOXNmXFycfOlZjnXr1lWqVAmDF8yV8BKdZbht/NFHH4naHj16VKhQoWLFiuK2MbKpX79+ogpTrdKlSycmJrZo0WLIkCGiUH3b2NHRUUzKFFFRUTVr1kQ0Y0Y2aNAgq7m7nNORI0cw4cLAhz8vT0blLnHw4jx27NgVK1YUqbe08+yrr776+uuvAwMDMatKS0vr2rXr999/L6owGBEjFHHb2MnJqXnz5snJyXgGHBwcMD5CYZcuXd57770TJ05gOlmuXDnltnG3bt1q1aqFlhguYeaFuKlTp87mzZvbt2+PZy/7wa0ZxrzLli3DqOfixYvyNUS2TWvixMbGTp48OTQ01EI/nmdUamrqoUOHsLBz5078i5gICAhQatu0aaPcNkYAVa9evYRBnz59xCzSz88Pky/MkjD5Qgwpt42RQQ0aNEBaIYaQNSKn8LyJR7Edt27dWrNmjYeHx+XLl+XriWzV8xPnt99+mzZt2qJFi27fvi1fU7YEkZGQkCC903Tt2rWUlBR1iSIpKen69etyqe3BcHjhwoULFizgx3no6XMT5+jRoxja4FVavo6IciMxMREX0pkzZ+QrjGyMucQJDg729/eXrx2ivMI8dMeOHfJ1RrbEZOJgBr5161b5kiHKn5CQkNDQUPlqI5thPHHCw8PFG8Z5s3///suXL8ulGnh7e0+fPl0uLXhGH3f79u1avqBgdNuczDQbPnx4fp5tfeGUC/q3QVauXBkVFSVfc2QbjCTO/fv3MeWWLxPN0FErVqyYt8Rp165dy5Yt5dKCZ/RxJ02a1KZNm1u3bknlEqPbCk5OTq1btxbLZpq99NJLyrvyf6HRo0fjaHEw4iOLBcrFxYU3km2TkcRZv359nr97mZGRUadOnW+++Uau0MZMtyxQRh/34sWL5cqV8/LyksolRrcVxo4d+69//Ussm2mWq8RZtGiR+DSQ7qpWrdq7d+/ixYsXQuLExMRwbmWbjCSOqcG/Fj4+PqVKlUpISFAXav8Ij7pbat9Kod7kjoGq8hlSlanHRd+uVKlSzu9eqtuY2laSM3GUxuYTR9pn165dGzZsqC7JyuXJmiKalS1bthASBzw8POQrj2yAkcRxd3eXrw7NMA357LPPlNXIyEislilT5pVXXlFmaugzw4YNE8sYRzRo0EB5R0x0S8RW3bp1sVXPnj2Vb2zduHFjwIABmK9Vrlx5/PjxTZs2FcmIwl69es2cObNmzZriS5ILFy7s1KlT+fLlq1SpgpZic6Wl0Z2Lxw0MDMRucagjR44U5WfOnLGzswsODhariB5MPd544w0MBF577TXxS12mts3KfkSxrE6c9PR0cS4YVuAIX3zxRSVxcFKOjo5iOeezN27cOCRCyZIl8aRhPJJl+Cb3oEGD0KBChQojRoxQPjNl5mTNK7TEmTFjhnzlkQ0wkjh5HuMgFIoVK6b+bjT6GPpGUFCQn58fJmuisHHjxl26dBHLycnJ6NKzZ88Wq+iWpUuXRmf+8ccf27ZtiyolMr788kv08/79+69Zswa5hirRS8Um4icjxId6Bw4cOHTo0LVr1/bo0QPNkBrP3TmqMIFCp8V8sFu3bqjav39/luFlHwMQ5ctT6MaowpHgXJBxmzdvNrOtqFJSRr2M/SBlBg8evHLlyhYtWijnkmUYJH788cdiOeezt3fvXgcHhxo1aqBkw4YNKMETgrjx9PTEDA5P/ty5c8W2Zk7WvEJLHI5xbJORxMnzfZxffvkFV7b6NzoRBB9++KH055/MJ0716tWVX8DCqz06ZJbh82PoTsroCa/keJ1XJ87hw4dFldrx48exc3ROsWpq56KqRIkSGzduzDL8+Ca2cnFxEVX169fv3LlzluEzxDiGevXqiXKFmW2NJg6GdYibL774QpTfunVLOReJ0WdPPasSh/Tdd9+JVTyxyGKxbOZkzSucxImJicEzJl95ZAOMJE6e36v66aef0N/Uv9Hp7OyMErzSenl5KV+YNp846psdmC9gXHPlypXt27ejmbe3t1Kl3PuQNskyTDSWLl2KgQkGC9hq3rx5otzUzqUqHCe2mjBhglht3br1+++/j4UdO3ag/B//+Ef2Dv7DzLZGEwfjFLTBnEKUZ5m+j2P02VMnjjgkJEtjA4RF7dq1RZWZkzWvcBKH71XZLCOJ8zSvn8eJi4uTcgFWrVrVtGlTlCuv6toTB/MO1KakpBw4cECdHVmmEwdDBrzOv/rqq3jlxxTDTOIoO5eqpNSoVasWOjkWtm3bhvIxY8Zk7+A/zGxrNHG2bNmCNuK3uwRTiZNl7NlTJ444JFT5ZFu2bJmoMnOy5hVC4vDzOLbMeOIAEie3nznOyMjABCfnKAD9sFevXniNFX8hs0mTJsrgH1ljJnHeeecdZEeWYQ+VK1fu0aOHKA8ICLBT3cdRbxIUFISqRYsWZWXPcdS3NozuXKpSpwbyC4ct3uHG3l544QUcfPYO/sPUtlKVsnzt2jVMo5R75+fOncOkzFTiZOV49pA4ykAGk00ckpj0ScycrHkFnTghISGcT9kyk4nzNE/fq+rZsyeudbF8584dTG2wk9OnT3fs2LFKlSriu9QDBw4sV67c6tWrMV7Ay7uUOC+++KKrq2tsbOzEiRPRFcVP8GUZfssGLZs1a9ahQwd7e3ssi1vUUtfC6AxVX3/99YYNG/CgWB46dKioMrNzU6lx5MgRLB88eFBUoedjFcG3a9cuPDOBgYFmtpWq1MsfffRRhQoVpk2btmDBAoyhlPQEHLb4uI2pZw/Hj/ahoaFILqz27dtX/FwhRg379u1Thk5mTtYUnOzPP/9cqlQpDJqwgCiUW+Qbv1dF5hIHoqOjp0yZomU0LqBz4pIVgyP0mbfffrtYsWLoIZhJif4JERER9erVQyGCY/369ViYM2eOqEI/cXR0RKygEK/eyC/lvin624gRI1q0aIE+tnv3buzW2dkZ5e3btxf3WRS9e/cWP2SDHovpm5Jo2Dm6t9Gdq3ciUgO9FMt/+9vf1H9JJjU1tXv37uKMMJoTd7tMbStVqZf37NlTtWpVtHz55Zd9fX3xjCmJM3LkSOw5y/SzFx8f7+DggEKR7Mid/v3742RRgohBHItmZk7WFIy87FSUd+h0geGYi4sLvztOz0kcuHv3rpeXF4YkGj9Ihhf/t956S/mRQFzo0gcCBaMvoejS4qUbm5i5zbl8+XJ0iZUrV8oV2S5cuCD2c8fwo8XiyJVRhvmdK9atW9ewYcOcLZF92Gc+fzkUm8fFxT33KxRmnj11gty8efPUqVPqTyrm9mQLjvh9HIxueKuYnmpJHAGD86lTp2q8s4MXf/RJuTR/xo4di9H+zJkzhw0b9uqrr+IFPLe/fCrNv54LZ2G5P+Rs9GQxVm1ijPYxbK4gAfHa4O7ujv3L1xPZKq2JI+CFdNasWZgKPffFWXfLli3r0aMHusd77703aNAgjNLlFs+DzFJ/INi6GT1ZRMAyY3J+jSOfMKqaO3cupr34b5KvIbJtuUscISkpydPTE5dUQf+sAVmc8PBwDGrmzJmDuad83RDlLXGEe/fuYbAze/bs1atX8wd9bRwm0d7e3hMnTty5cyfv15AZeU8cxeXLl+fPnz9jxow1a9akpqbKFyNZr6ioKMyyp0+fjledzMxM+cogykGHxFFg9h4QEDBz5kwfHx9L+bvjlFtpaWlbt251NRDfJpWvAyLT9Ewcxd27d7ds2eLl5YX08ff3j42NlS9bsijXrl3bvn27u7v7tGnT5s6di5eTx48fy//rRBoUSOKopaenI30QPR4eHgsWLAgPD8eLpHxFU9ETHx+PIQxSxs3Nbfbs2REREX/88Yf8v0uUSwWeOGp37tw5cODAvHnzZsyYgcn/kiVLoqKicvuxGiogCQkJGzduFP81np6eQUFBZ8+effLkify/SJQPhZo4Egx2wsLCZs2ahesbIyBvb29c8SdOnND44WbKjwsXLuzatcvPzw/PPAYy+C9Ys2YNxjUPHz6U/5+I9PNXJo7kwYMHuOI3b97s5eUlMggCAgIwLOIHf/LjypUrR44cWb9+PcJdPKt4ev39/THAvH37tvzfQFSQilDiGJWSkhIREbFq1SoRQ4Bhv6+vb3BwcHh4uPIbd5Rl+LEhJMumTZswXUWszDDAM4bV3bt3Y9J0//59+fklKlxFPXGMyjLcdDh48GBgYODMmTO9DEQYAV69Q0ND9+3bFxsbK35TxjpgqIKExXkhUxDByvliQTwDKNy7dy/OOj09XX7KiIoGi0wc81JTU9Ez8WqP3FmxYoW3t7fokCD6p+iosHr1avTSsLAwdONDhw6dzfbcH3bIp7S0NOWxDh8+jEfHMeBIcDzqHBHEkeMsMFTBzAjnFRcXh6Ef358mS2SFiaMdRg3oujExMejGP/30E2Zq6w38/PwwdPJWUTLLqOrVq8tFOSi7wp4XLFggHgiPiLjBo0dHR6cY8L0hsm42nTh6QeLIRURkDBNHB0wcIo2YODpg4hBpxMTRAROHSCMmjg6YOEQaMXF0wMQh0oiJowMmDpFGTBwdMHGINGLi6ICJQ6QRE0cHTBwijZg4OmDiEGnExNEBE4dIIyaODpg4RBoxcXTAxCHSiImjAyYOkUZMHB0wcYg0YuLogIlDpBETRwdMHCKNmDg6YOIQacTE0QETh0gjJo4OmDhEGjFxdMDEIdKIiaMDJg6RRkwcHTBxiDRi4uiAiUOkERNHB0wcIo2YODpg4hBpxMTRAROHSCMmjg6YOEQaMXF0wMQh0oiJowMmDpFGTBwdMHGINGLi6ICJQ6QRE0cHTBwijZg4OmDiEGnExNEBE4dIIyaODpg4RBoxcXTAxCHSiImjAyYOkUZMHB0wcYg0YuLogIlDpBETRwdMHCKNmDg6YOIQacTE0QETh0gjJo4OmDhEGjFxdMDEIdKIiaMDJg6RRkwcHTBxiDRi4uiAiUOkERMnL5KTk9WrUuJItUSkYOLkxeLFiyMjI5VVdeKgHLXKKhGpMXHyIiMjo1GjRsHBwWJVSRzEzdtvv43a/zYlIhUmTh6NGjXK3t5+zZo1T7MTB3GDkn79+slNiSgbEyePkpKSahggdJA4iJvatWtjAeVyUyLKxsTJu759+7777rsIHQRNnTp18G/Hjh3lRkSkwsTJu4iIiPfffx9BI0Ln9ddf379/v9yIiFSYOPnSoUOHVq1aVTdwcHCQq4noWUycfAkKCnJyckLc1KxZ09/fX64momcxcfLlwYMHjo6ODRo0qFOnDpblaiJ6FhMnv+bMmfP6669PnjxZriCiHGw6ca5cuZKSkhITE3PkyJF9+/atN8BEacGCBd4GXiqeJri6utauXRv/yhUq6v1gt76+voGBgXggPNzevXsPHz4cHR2dYvDkyRP5KImsiBUmTmpq6tmzZ9GNN2zYsHz5chEcoud7eHjMmDFD+XeVwa5duxA3hw4dOpvtxo0bWblx4MABucistLQ05bFwnHj0sLCwlStX4mDEgYkjBHVmLV68GCGF9nFxccimx48fy2dOVORZZOKg0yYkJISHh2OkoASK0lf9/f2RNejGsbGxly5dkru7xcKIDAmF89q4caOSTUow4UlAZu3ZswdnnZ6eLj9lREVDUU8cvJgfPHgQfUlkiuhmPj4+mI8gcdAD5X5pw5KTkzE9RB4tWrRIhK+IJAyOdu/ejYy+f/++/PwSFa4ilDgPHjyIj4/fvHmzMmBxd3fHgGX//v1JSUly9yLNMDhCEiGjZ82a5W6Ap3fFihWRkZG3b9+W/xuICtJfmThpaWm7du1CNxD5gnlBaGjoiRMn7ty5I3caS5CZmenr62spw64LFy7gyV+wYAECaPr06fgvWL16dVxc3MOHD+X/JyL9FGriIEowYJk3bx6ubzc3Nwz+o6KiMjIy5N7w1wkLC2vatOmOHTuUksGDB2MSp2pi0rFjx+zs7KKjo+UKC4FpFxIf0S8CKDAw8MyZM3zvjPRV4ImTkpKC8fzUqVOnTZuGIUB4eDiGNvLFXmS4uLggNZycnJSSatWqrVu3TtXEpKVLl5YpUwYjHbnCMmGGu2HDBpE+GArFxMT8+eef8v8uUS4VSOLget20aRMuVnTghQsX4sVfvpyLqq5du5YvXx6hExkZidVz585hWZkoIXqGDRs2YsSIvXv3ihKM2hA0AwcOxNxk6NChrVq1UsqXL1+Ocmdn5/T0dFFoufAisW/fvjlz5mDmO3/+fKSP/F9OpI2eiXP+/HlclK6urjNnzsRFKV+2RdLs2bMx+Dp8+PAnn3yCVXt7+7lz55YtW7Zv375YDQoKqly5smj5zTffVKlSZeTIke3atStRosTx48dROGDAAAyCfvjhh8aNGyObhg8fLhqjvEmTJuPHj69ataqnp6e/v/+4ceOQXx07drx58+Z/HtsyIX22b9+Ok5o1axYW7t27J18HRKbpkDgIGvTSKVOmrFixAsvyFVqETZgwoWXLlsHBwQiagwcPXrhwAalx8uRJjGJKliyZkJCAmOjUqRNaIkCRMmLgg/FLsWLFMKhB6BQvXlyM4G7dulWqVKnFixdjGc0wvUK+oGWvXr1Gjx6NZQydMDJq27Yt4umZg7Bkp06dwpDHw8Nj7dq1d+/ela8Mohzynji44JYtW4ZOi3+vXr0qX4xF3qhRozAwCQkJwYgmPDw8yzATRC4gJuLj4xEliIbOnTsjL1DVvXt3hIXYMDMzE+Hi5ub25ZdfKoXitvHRo0ex/Pnnn9upfPvttyjEfurXr49cQ1qJ5LImiFRfX1+McPfv3//o0SP5WiHKlpfEiYuLmz59ure3NxbkS89yrFu3rlKlShi8YK6El+gsw23jjz76SNT26NGjQoUKFStWFLeNkU39+vUTVZhqlS5dOjExsUWLFkOGDBGF6tvGjo6OYlKmiIqKqlmz5vLlyzEjGzRokNXcXc7pyJEjmHBh4MOflyejcpc4eHEeO3YsZk9F6i3tPPvqq6++/vrrwMBAzKrS0tK6du36/fffiyoMRsQIRdw2dnJyat68eXJyMp4BBwcHjI9Q2KVLl/fee+/EiRP+/v7lypVTbht369atVq1aaInhEmZeiJs6deps3ry5ffv2ePayH9yaYcyLCPbx8bl48aJ8DZFt05o4sbGxzs7OoaGhFvrxPKNSU1MPHTqEhZ07d+JfxERAQIBS26ZNG+W2MQKoevXqJQz69OkjZpF+fn6YfGGWhMkXYki5bYwMatCgAdIKMYSsETmF5008iu24devWmjVr3N3dL1++LF9PZKuenzi//fbbtGnTFi1adPv2bfmasiWIjISEBOmdpmvXrqWkpKhLFElJSdevX5dLbQ+GwwsXLlywYAE/zkNPn5s4R48enTx5Ml6l5euIKDcSExNxIcXHx8tXGNkYc4mzfv16f39/+dohyivMQ7dv3y5fZ2RLTCYOZuBbt26VLxmi/AkJCQkNDZWvNrIZxhPnwIED4g3jvNm/f//ly5flUg28vb2nT58ulxY8o4+LV2MtX1Awum1OZpoNHz48P8+2jjD3iY6OLugvvq1cuTIqKkq+5sg2GEmc+/fvY8otXyaaoaNWrFgxb4nTrl27li1byqUFz+jjTpo0qU2bNrdu3ZLKJUa3FZycnFq3bi2WzTR76aWXlHfl/ypTp061t7cXHwioUKHCihUr5Ba6cnFx4Y1k22QkcYKCgvL83cuMjIw6dep88803coU2ZrplgTL6uBcvXixXrpyXl5dULjG6rTB27Nh//etfYtlMs1wlzqJFi8SngfQ1bNiwuXPnXrp0CS8YtWvXxombeg9OFzExMZxb2SYjiWNq8K+Fj49PqVKlEhIS1IXaP8Kj7pbat1KoN7ljoKp8hlRl6nHRtytVqpTzu5fqNqa2leRMHKWx+cSR9tm1a9eGDRuqS7JyebLPNWHCBIx0wsLC5ApdeXh4yFce2QAjiePu7i5fHZphGvLZZ58pq5GRkVgtU6bMK6+8oszU0GfwiiqWMY5o0KCB8o6Y6JaIrbp162Krnj17Kt/YunHjxoABAzBfq1y58vjx45s2bSqSEYW9evWaOXNmzZo1xZckFy5c2KlTp/Lly1epUgUtxeZKS6M7F48bGBiI3eJQR44cKcrPnDmDvhccHCxWET2jR49+4403ihcv/tprr4lf6jK1bVb2I4pldeKkp6eLc6latSqO8MUXX1QSByfl6OgolnM+e+PGjStbtmzJkiXxpPXu3TvL8E3uQYMGoQGmQiNGjFA+M2XmZJ8Lgx2ctfKLHAVkxowZ8pVHNsBI4uR5jINQKFasmPq70ehj6BuYpvn5+a1fv14UNm7cuEuXLmI5OTkZF/fs2bPFKrpl6dKl0Zl//PHHtm3bokqJjC+//BL9vH///mvWrEGuoUr0UrGJ+MkI8aHegQMHDh06dO3atT169EAzpMZzd44qzCPQaTEf7NatG6r279+fZRgdYACifHkK3RhVOBKcCzJu8+bNZrYVVUrKqJexH6TM4MGDV65c2aJFC+VcsgyDxI8//lgs53z2kAIODg41atRAyYYNG1CCJwRx4+npiRkcnnyEhdjWzMmal5mZiaf35Zdffu4NrHxi4tgmI4mDiztv93F++eUXXNnq3+hEEHz44YfSn38ynzjVq1dXfgELr/bokFmG91DQnZTRE17J8TqvTpzDhw+LKrXjx49j5+icYtXUzkVViRIlNm7cmGX48U1s5eLiIqrq16/fuXPnLMNniHEM9erVE+UKM9saTRwM6xA3X3zxhShHx1bORWL02VPPqsQhfffdd2IVTyzCQiybOVnzMJHE6SgvDwUkJiYGz5h85ZENMJI4Dx48UPpMrvz000/ob+rf6HR2dkYJXmm9vLyUL0ybTxz1zQ7MFzCuuXLlyvbt29HM29tbqVLufUibZBkmGkuXLsXABIMFbDVv3jxRbmrnUhWOE1tNmDBBrLZu3fr999/Hwo4dO1D+j3/8I3sH/2FmW6OJg3EK2uAVXpRnmb6PY/TZUyeOOCQkS2MDTLhq164tqsycrBkTJ05EM/ErPwWK71XZLCOJA+Hh4Xn4hEhcXJyUC7Bq1aqmTZuiXHlV1544mHegNiUl5cCBA+rsyDKdOBgy4HX+1VdfxSu/uB9hKnGUnUtVUmrUqlULnRwL27ZtQ/mYMWOyd/AfZrY1mjhbtmxBG3WvNpU4WcaePXXiiENClU+2ZcuWiSozJ2sKQvCFF15AWMsVesNc8ujRo/I1R7bBeOIAEie3nznOyMjABCfnKAD9sFevXnjxFH8hs0mTJsrgH1ljJnHeeecdZEeWYQ+VK1fu0aOHKA8ICLBT3cdRbxIUFISqRYsWZWXPcdS3NozuXKpSpwbyC4ct3uHG3tAhcfDZO/gPU9tKVcrytWvXMI1S7p2fO3cOsxhTiZOV49lD4igDGUw2cUhi0icxc7JG7dy5E7vSeK8nP0JCQjifsmUmEwdwcah/vUGLnj174loXy3fu3MHUJjg4+PTp0x07dqxSpYr4LvXAgQPLlSu3evVqjBfw8i4lzosvvujq6hobG4sRPrqi+Am+LMNv2aBls2bNOnToID6rJm5RS10LozNUff311xs2bMCDYnno0KGiyszOTaXGkSNHsHzw4EFRhZ6PVQTfrl27/P39AwMDzWwrVamXP/roowoVKkybNm3BggUYQynpCThs8XEbU88ejh/tQ0NDkVxY7du3r/i5wqioqH379ilDJzMna5Sjo6OdYXy6KFtB/FVCPz8/RJt8nZEtMZc4EB0dPXXqVPOjcTV0zlKlSonBEfrM22+/XaxYMVzKmEmJ/gkRERH16tVDIYJj/fr1WJgzZ46oQj/BpY9YQSFecpFfyn1T9LcRI0a0aNECfWz37t3YrbOzM8rbt28v7rMoevfuLX7IBj0W0zcl0bBzdG+jO1fvRKQGeimW//a3v6n/kkxqamr37t3FGWE0J96xNrWtVKVe3rNnT9WqVdHy5Zdf9vX1xTOmJM7IkSOx5yzTz158fLyDgwMKRbIjd/r374+TRQkiBnEsmpk5WaMwhLR7FqZscqN8wHDMxcXlzJkz8hVGNuY5iQN3797FS9+aNWs0fpAML/5vvfWW8iOBuNClDwQKmE3IRYYuLV66sYmZ25zLly9Hl1i5cqVcke3ChQtiP3cMP1osjlwZZZjfuWLdunUNGzbM2RLZh30qt3LzBpvHxcU99x1oM8+eOkFu3rx56tQp9ScVc3uyBUf8Pg5GN7xVTE+1JI6A7oHBjsbXPfFLKHJp/owdO/aLL76YOXPmsGHDXn31VbyA5/aXT6X513PhLCz3h5yNnizGqk2M0T6GzRUk4IoVKzw8PLB/+XoiW6U1cQS8kM6aNQtToee+OOtu2bJlPXr0QPd47733Bg0ahFG63OJ5kFnqDwRbN6MniwhYZkzOr3HkE0ZVc+fORWQnJSXJ1xDZttwljpCcnOzl5TVv3ryCuLlIFi08PByDmjlz5mDuKV83RHlLHOHevXvBwcGzZ89evXo1f9DXxmES7e3tPXHixJ07d/J+DZmR98RRXL58ef78+Z6enmvWrElNTZUvRrJeUVFRmGVPnz4d//WZmZnylUGUgw6Jo8DsfeXKlbgEfXx8LOXvjlNupaWlbd26dYqB+DapfB0QmaZn4iju3r27ZcsWDLNnzpzp7+8fGxsrX7ZkUa5du7Zjxw53d/dp06bNnTsXLyePHz+W/9eJNCiQxFFLT0/HSyKiZ8aMGQsWLDh48GBB/4wu6SI+Pj40NNTDw8PNzW327NmHDh36448/5P9dolwq8MRRu3PnzoEDB8RNH0z+lyxZcvTo0dx+rIYKSEJCwqZNm/DCgLEM/oOCgoLOnj375MkT+X+RKB8KNXEkGOyEhYXh9RPXNy50zMJwxZ84cULjh5spPy5cuIAn38/PD6MYETFr1qzBuObhw4fy/xORfv7KxJE8ePAAV/yWLVu8vLxEBqEzBAQEYFjED/7kx5UrV44cOSI+yuBhgKfX398/Kirq9u3b8n8DUUEqQoljVEpKSkRExOrVq0UMiSTy9fUNCQk5ePCg8ht3lGX4sSEky+bNmzFdxbMkni7A6p49ezBpun//vvz8EhWuop44RmUZbjogcYKCgmbOnInpmDIsAgyLNm7c+NNPP8XGxorflLEOGKogYfft24dMwQxInK/IFG+DVatW7d27F2ednp4uP2VERYNFJo55qamp6Jl4tQ8NDcXcQUSSSCWli4pswtAJXTcsLAzx9PPPP5/NZv6HHfIvLS1NeazDhw/j0Xfv3i0ORjk2LPz973//f//v/3kZ4CwwVMHMCOcVFxeHoR/fnyZLZIWJox1GDei6MTEx6Mbo9sHZ/Pz8ZmYTaaUmIsDDw6NHjx5i2Sh5MwOxzwULFiiPhTELHj06OjrFQP3eUGJiYvv27b/77rvff/9dddREFsymEyc/Ll682LJlS7lUb3/88ceoUaM+/PDD+Ph4uY7IAjFx8ujgwYOY9cilBWPTpk2NGzdeuXKlXEFkaZg4ebR69WqMPuTSAnPhwoXOnTvzVzvJ0jFx8sjNzc3Hx0cuJSKzmDh5NGTIkK1bt8qlRGQWEyePPv7441OnTsmlRGQWEyeP1q1bd/fuXbm0cKWlpcXExMilREUYE8eCIW4cHBx8fHz4DW+yFEwcy3b9+vXPP//8yy+/vHnzplxHVPQwcSzeo0ePPD09mzVrdvjwYbmOqIhh4liJiIiId955JzQ0VK4gKkqYONYjPT2dcysq4pg4RFR4mDh54ebmlpGRIZcS0fMwcfKiTp06lvILEvfu3ZOLiP46TJxcu3nzZqNGjeTSIun69euOjo67d++WK4j+IkycXDtx4kTnzp3l0qLql19+adGixeTJk/nnwKkoYOLk2rZt24YMGSKXFmFZWVlff/01UvLSpUtyHVHhYuLkmp+f39SpU+XSIm/FihWNGzdGXMoVRIWIiZNr48eP9/f3l0stQVxcXEBAgFxKVIiYOLl26tSplJQUuZSINGDiEFHhYeIQUeFh4ti0x48fjxkz5ty5c3IFUcFg4ti6kJCQJk2arF27Vq4gKgBMHHp6/vx5JyenESNG/OW/o0pWj4lD/+f+/ftjx4794IMP4uLi5Doi/TBxcsff3z8sLEwutRZbt27t0KEDvw9BBYeJkzvDhg3btGmTXGpFHj16JBcR6YeJkztdu3blH2whyjMmTu68++67qampcikRacPEyQXMOGrVqvXw4UO5wqqdPn369u3bcilRnjBxcuHq1avNmjWTS62dj49P8+bNo6Oj5Qqi3GPi5EJMTMynn34ql9qA/fv3Ozo6zps37/Hjx3JdoZs9e/aWLVvkUrIQTJxcyMrKOnv2rFxqG65fv969e/fevXunp6fLdQUs2kBZ/fDDD8eOHauqJ0vCxCGtHj165O3t/c4771y8eFGuK0itW7ceMGCAXJoP/DPtfyEmDuXOyZMntc+tUlNTe/XqVaVKlZdeeumtt966f/8+Cq9cuYLh0isGPXv2vHr1qmjctWvXKVOmjBo1qlq1apjE/fTTTygcM2YMtn355Zex+Zo1a0QzNzc3U+2hTZs2vr6+YhkzwXbt2olluHTp0scff1yqVKk33nhj8+bNSjkVGiYOFZSHDx9iQFSpUqXZs2djNrp169ZHBs2aNWvYsOGWLVvQ57HQvHlzEWFNmjQpUaJE586dN27c2LJlyw8++ACFFy5caNq0aZcuXWJiYtLS0kSz7777zlR7qFGjhqurq1h2dna2t7cXy3gUBFOnTp2OHz/+z3/+s3r16qKcChMThwpKVFSUnZ3d6NGj1YVHjx5F4ZIlS8TqokWLsHrs2LGnhgT56KOPxHcsPDw8SpcuLdpIsyp14hhtbypxxPHMmDHjyJEjISEhWD516pSookLDxKH8wtjB6GeU1q9fj169Y8eOnIW//PKLWEXWYHXDhg1PVVECc+fO1ZI4RtubShzx0HXq1GmcLTQ0VFRRoWHiUH6h33766ac5P4odGRmJHj5p0iR14aFDh1C4evVqserv749VtHxqOkGQOP379xfL6mam2iNxJk6cKJa/+uoraYyzbNkysUp/CSaOVjdv3vzf//1fuZQMMDlC/5e+VY+Bj4ODQ/Xq1ZEvN27c2LZt27179+7fv49RRps2bc6cOYNJTatWrerVqyfuKJtKkH79+tWvXz8zM1OsPjdx2rZt+8EHH2DnP/74IyJGSRxxX+mtt97as2cPHhENLl++LKqo0DBxtIqNjf3/7d15XFRVwwdwt3pzeVvNUB41l3qMRSmXtDRLfNLsoz2aKW7llppPZeaeSyKIrCoiuAOBguC+4pKBpiBoKm5sKoqIIrigYqGp7++dk/eZDszMhYFxBn7fP/jcOefMnTvDPb97zp2ZO507d5ZL6bEjR44U/vHPtLQ0FFbSqFevHnLnkebHMDCjEYUtWrRITEwUje3t7b/55huxPH/+/Bo1aohljIAaNmxYrVq15cuXazfT1X7Tpk116tSpXLky/l9ooCQOpKend+zYEY+L2hdeeAEtlSoyDSaOWrt27cLBVi4lLXl5eUOHDv3www+lH//E8PDatWvaJXD16tXc3FypUJeHDx9mZGSov5IGUq/wIyqwnVlZWXIpmQQTR62VK1eOGzdOLqVCVqxYsX//frmUSIOJo5a3t7eXl5dcSkTFwcRRa/z48co7LERUMkwctQYNGrRz5065lIiKg4mj1tWrV8WbuFRc2dnZ06dPz8/Plyuo4mHiUJm7e/fud99916FDB/74JzFxyETWrFlja2vLH/+s4Jg4ZDpnzpxxdHT86quv+OOfFRYTh0xK/Pjn2LFj5QqqGJg49ATwZz8rLCYOEZkOE0eV0NDQ+fPny6VEVExMHFVcXFyUS+dSWUhISOAFzysCJo4qY8aMCQ8Pl0uplNy/f79Hjx4DBgzQ84VvKh+YOKoMHDhw9+7dcimVHoSOq6vrW2+9dfDgQbmOyhEmjipdu3Y9cuSIXEqlLTo62nx+/JPKAhNHldatW2dkZMilVAYuX77cq1cvJycn9dffIgvCxFGlUaNGd+/elUupbCBrDhw4IJdSucDEUeXkyZNyEREVHxOHiEyHiUOWocgf4SOLw8Qhy/Cf//zH2dlZV+7wNLOlYOKQZbhx48bnn3/erVu3ixcvynWPHh04cIBXwLAITByyJEuWLLGzs9u+fbtUnpSU5O7uLhWSGWLikIU5duxYmzZtfvjhh4KCAqXw3r17TZs25e/emT8mjmG+vr7btm2TS+nJycvLGz9+/NWrV7ULtX8UmMwWE8ewMWPGREREyKVkZj744IO6devyk1Nmjolj2ODBg6OiouRSMjMjRoywtrbu1auXXEHmhIljGHZifuje/C1evNjKysrGxobf8jdnTBzDHB0dOVY3f8ePH0fi1K9fv127dvx4jtli4hjWqlUrfnHcfFy8eDEoKKhv374NGjSwKoq9vT1/Id5sMXEMe/311/Py8uRSetLy8/PDwsJ69+7dqFGjxo0bW1tbi8SpW7cu5lb8QKB5YuIYlp2dzUvwmjP8dxISEmbPnt22bdsmTZq0bt0af7U/EIhhEUap8fHx+/fv3717d3h4eJjGwoULPbS4F2WOXnJrDWWFCxYsEA8EO3fuxKPHxcVlaFTYPYqJQ5YqMzPz9OnT6MaRkZHLly8XkfHDDz/069evTZs2DRs2xLKbRrDG9u3bETf79u07/diVK1fyyhKOVcpjibCLiooSGyM2DBBb4q/ILzyLRYsWIaHQ/vjx48imcnY5RCYOmS902uTk5Ojo6FWrVolAET1T9NUVK1asWbMG3Rg98/z581JvR5qkpqZKhRYBIzIkFJ7XunXrRDZh+CZSSYyhULhjx47ExETpM5AWgYlDZgEH85iYGPSlx/OV/48VzEpWr16NxEEPlPtlBZaWlnbgwAHkEUZDIoxEHuEmxlBJSUl//PGH/PqaDSYOmVpBQcGJEyfWr18vYgV9xtXVFQOWPXv2WOioxExgcIQkQkZ7eXnN1sArjPkmCq9fvy7/G54QJg6Vuezs7G3btqEbiHzBvGDt2rVHjhy5efOm3GkswY0bN/z8/Cxl2HXu3Lnt27djg101kEEhISGYh+q60lBZY+JQ6UOUYMAyb948RIyLiwtG+3Fxcbm5uXJveHIw+7C3t9+6datSMmzYMEzitJrodOjQoUqVKsXHx8sVFiI5ORmJj+jHvwYBFBYWdurUKZO9d8bEMWDLli1jx46VS6mQCxcuhIeHOzs7z5o1C0fU6OhoDG3knd1szJgxA6nRqVMnpeSVV15ZtWqVVhOdli5dWqNGDYx05ArLdPLkyTVr1ojhz8KFC5Gk9+7dk/+7pYeJYwD+GV9//bVcShrYX9etW4edFR04ICAAB395dzZX3bt3f/bZZxE6sbGxuJmUlIRlZaKE6Bk5cuTo0aN37dolSjBqQ9AMHjwYQ4MRI0a0bdtWKV++fDnKp02bdvXqVVFouXCQ2L17t4+PD2a+vr6+SB/5X240Jo4BK1euHD9+vFxasZ05c2bu3LlIGS8vr4SEBHm3NUvoRRh87d+//+OPP8bN+vXrY9JXs2bN/v374yZGZ7Vr1xYtv/zyy5dffnnMmDEdO3asVq3a4cOHUfjFF19gEITRrq2tLbJp1KhRojHK7ezsJk+eXKdOHfTSwMDASZMmIb8cHR1zcnL+emzLhPTBAB9Pytvbe/Pmzfn5+fJ+UCJMHAOWLVuGw5dcWiGlpaUhaGbOnLlixQqEjryHmrEpU6a0adMmIiICQRMTE3Pu3DmkxtGjRzGKefrpp5OTkxETnTt3RksEKFJGDHwwfqlSpQoGNQidqlWrihHctWvXnnnmmcWLF2MZzTC9Qr6gZZ8+fXBkwjKGThgZvffee4inv22EJTt27BiGPG5ubqGhoUZ+fYSJY4C/vz+OjXJpRYLuhNjFYRx/L126JO+MZm/cuHEYmERGRmJEEx0djZL169cjF/C8Tp48iShBNHTp0gV5gaqePXsiLMQdb9y4gXBxcXFxcnJSCsVp44MHD2L53//+dyUtX331VZ7m4oSvvfYacg1pJZKrPEGk+vn5YcD4888/l+wL+kwcA+bPnz9nzhy5tGI4ceKEq6urp6cnFuRdz3KsWrXqxRdfxOAFcyXMkfM0p407dOgganv16vXcc889//zz4rQxsmngwIGiClOt6tWrp6SktG7devjw4aJQ+7RxixYtxKRMERcXZ21tvXz5cszIhg4dWm7OLhd24MABTLjQOzB5lPcbvZg4Bnh5eSHR5dLyDvvThAkTMHsyq7e0S2zAgAFDhgwJCwvDrCo7O7t79+7ffvutqMJgRIxQxGnjTp06tWzZEvNHDE+aN2+O8REKu3Xr1qpVqyNHjgQGBtaqVUs5bfzJJ580aNAALTFcwswLcdO4ceMNGza8//77EydOfPzg5RnGvIjgBQsWYKIq70M6MHEMK9no0UIlJiZOmzZt7dq1FvrxvCJlZmbu27cPC9u2bcNfxERQUJBS2759e+W0MQLIysqqmka/fv3ELBIza0y+MEvC5AsxpJw2RgY1a9YMaYUYQtaInMLrJh6l4rh27VpISMjs2bPPnz8v70+FMHHoL9h1nJ2dFy1adP36dXmfqkgQGcnJydI7TVlZWRkZGdolitTU1MuXL8ulFQ+Gw4hmPz8//R/nYeLQ/8OMYPr06ThKy/sRUXGkpKRgR9JzlV4mDj1avXp1YGCgvO8QlRQGO1u2bJH3Mw0mTulzd3cfr0X8uh4KFy5cqN3Mx8cnOjpau/3EiROxvG/fPu1mZQ0z8E2bNsm7DJFxIiMj165dK+9tTJyy0KRJk1dffbXfY+Hh4aKwUqVKO3fuVJrZ2dm5a66MKdr36dOnQ4cOVlZWaPbll1+a5hInv/zyi3jDuGT27Nlz4cIFuVQFT09PV1dXubTsFfm4OBqr+YJCkfctTE+zUaNGGfNql5acnJyEhIT4+PiyvgRiUFAQZuvSLsfEKX1IkCFDhhQuRJQgWe7cuSNKtBNHaX/v3r2vv/4aLU3wIaDff/8dU255N1ENHfX5558vWeJ07NixTZs2cmnZK/Jxp06d2r59+2vXrknlkiLvK3Tq1Kldu3ZiWU+z//mf/1HelX9SnJ2dq1atKj4QUKdOHRwO5RalCjuYdCKZiWMAjld+fn5yqV66Eufdd9/Fvxn7nCgpMnHgwYMH9vb2GOwoJWUEe1uJv3uZm5vbuHFjjMXkCnX0dMsyVeTjpqen16pVy8PDQyqXFHlfAdPh7777TizraVasxFm0aJH4NFDpwiBrwYIFqampUVFRVpofFJRblCoMpqS5FRPHAPHBSrlULyTIO++8s0JDTKlEoa+v72effSY+/P5Id+LA5MmTkU046moXljpdg381sNc+88wzycnJ2oXqP8Kj3S3V30uhfZebGlqVfyNV6Xpc9O0XX3yx8Hcvtdvouq+kcOIojfUnjrTO7t27v/HGG9olecV8sgYNHjwYu1lZX1TEzc1Ne69j4hjg4+Pj5eUll+qFBKlXr15XjQEDBiiFSJyrV6++/PLLOLAUFBToSRyUY1e4dOmSdmGpmz17trx3qIZpSI8ePZSbyFDcrFGjxgsvvKDM1NBnRo4cKZYxjmjWrJnyjpjologtPHfc69NPP1W+sXXlypUvvvgC87XatWsjeTHcE8mIwj59+uB/YW1tLb4kGRAQ0Llz52effRYvKVqKuysti1y5eNywsDCsFps6ZswYUX7q1Cm84BEREeImomf8+PGYAmMCgn+luFKXrvvmPX5EsaydOPh3i+eC+Qu28KmnnlISB0+qRYsWYrnwqzdp0qSaNWs+/fTTeNH69u2bp/km99ChQ9HgueeeGz16tPKZKT1P1iAHBwc8O7m0tEnnB5g4BiAmintKpXCCiEKs6pHmgjvYuWfMmKEncTBbwThfu6QslHiMg1DASE37u9HoY+gbGND5+/uvXr1aFNra2nbr1k0sp6Wl4VkjvsVNdMvq1atjd//+++/fe+89VCmR4eTkhH4+aNCg0NBQ5JqYhyp3EZeMEB/qxSF6xIgRmCb06tULzZAaBleOKryw6LR4hT/55BNU7dmzJ08zOsAARPnyFLoxqrAl4prBGzZs0HNfUaWkjPYy1oOUGTZsWHBwcOvWrZXnkqcZJH744YdiufCrt2vXrubNm9etWxcl2GFQghcEcYMdBjM4vPjz5s0T99XzZHU5duzY4sWLEWR4OpGRkXJ1aWPiFA8OpMX97njhBBGFInEA/2zsiDiIFZk4ubm5OG7jwKWUlBHs3CU7j/Pbb79hz9a+RieC4N1335Xe+9CfOFZWVsoVsHC0R4fM03x+DN1JGT3hSI7jvHbi7N+/X1RpO3z4MFaOzilu6lq5qKpWrdq6devyNBffFNEvql577bUuXbrkaT5DjG1o2rSpKFfouW+RiYNhHf7LvXv3FuWYIyvPRVLkq6c9qxKb9PXXX4ubeGGRxWJZz5PVBS9vJQ1EXuFf3SldCQkJeMW09zomjgFLly7FQFcu1QsJgsHtucfE5Eg7cTBoxzAb/3IlcUR7HHwwaMeuj2lCZmam9jrLAmZ2Sp8plp9//hkbr32NzmnTpqEER1oPDw/lC9P6E0f7ZAfmCxjXXLx4ccuWLWjm6empVCnnPqS75GkmGvjvYGCCnoN7zZ8/X5TrWrlUhe3EvaZMmSJutmvX7u2338bC1q1bUf6f//zn8Qr+oue+RSYOximVNO85ivI83edxinz1tBNHbBKSxVYDx6qGDRuKKj1PVg8k5ooVK7CbYYqq5pMBJfbjjz/yvariCQwMxI4ll+rVRPNGuOLNN98UhRgUKG0Q/KjCHqbdHsdA7E+jRo3CMEdpWaZiYmJK8AmREydOVPp7LsBPP/1kb2+PcuWorj5xMO9AbUZGxi+//FJJKzvydCcOhgw4zr/00ks48mOKoX0vXSuXqqTUaNCgATo5FjZv3ozyCRMmPF7BX/Tct8jE2bhxI9qIa3cJuhInr6hXTztxxCahasFjy5YtE1V6nqxBP/zwAxpjO+WKUoIndfDgQWmXY+IYoP9raeUAhirF/cwxAhETnMKjAPRDTAZxjBVjdTs7O2Xwj6zRkzgODg7IjjzNGmrXrt2rVy9RHhQUVEnrPI72XcLDw1G1aNGivMdzHO1TG0WuXKrSTg3kFzZbvMONtVWuXBkb/3gFf9F1X6lKWc7KysIhRDl3npSUhEmZrsTJK/TqIXGUgQwmm9gkMemT6HmyBnl7e+NZIBfkitKwZs2a9evXy3sbE0cl7A11/w5z71q1akmFU6dOle9phMIPCuPGjZOLjH7cyMhI7as3qIFpIPZ1sXzz5k1MbSIiIhITEx0dHTFWF9+lHjx4MF6ikJAQjBdweJcS56mnnsKQ+/jx4zjSoiuO11yCL09zLZtKmoHhBx98UL9+fSyLU9RS14qOjkbVkCFDsGfjQbE8YsQIUaVn5bpS48CBA1jGiE9UoefjJoJv+/btGORiqqvnvlKV9nKHDh2ee+65WbNmLVy4EGMoJT3zNB1SfNxG16uH7Uf7tWvXIrlws3///uJyhXFxcbt371aGTnqebJHwXHCAwUNgAovBNY4ciEK5kdECAgLEl3sKY+Ko8ueffx79u99++23nzp1SYemefCn8oHDhwgW5qDQeNz4+3tnZWeVoPE9zHZlnnnlGDI7QZ/75z39WqVIFPQQzKdE/Ye/evU2bNkUhgmP16tVYmDt3rqhCP2nRogViBYU4eiO/lPOm6AyjR49u3bo1+tiOHTuw2mnTpqH8/fffF+dZFH379hUXskGPxfRNSTSsHN27yJVrr0SkBnoplv/1r39p/5IMXs+ePXuKZ4Q+Kd6x1nVfqUp7GXuIOGH3v//7v35+fnjFlMQZM2YM1pyn+9U7efJk8+bNUSiSHbkzaNAgPFmUIGIQx6KZnidbJCcnp0qPId1CQ0PlFsbBcGzGjBmnT5+W97DHmDj0l9u3b3t6emIXVPlBMhz8X3/9deUigdjRpQ8ECkUeQtGlxaEbd9FzmnP58uXoGMHBwXLFY+fOnRPruam5aLHYcmWUoX/lCswr33jjjcItkX1Yp3Iqt2Rw9xMnThj8CoWeV087QXJyco4dO6b9ScXiPlk4e/YsRog4xhT+xKMxsCdgkuvv76//RAQTh/4G3QOzgM2bN8s7VFHElVDkUuNMnDixd+/eXl5eI0eOfOmll3AAL+6VT6X5l0F4FpZ7IecinyzGqnZFUT+GLRYk14oVK9zc3LB+eX8qhIlDRUhMTMQMJSIiwuDBudQtW7asV69e6B6tWrUaOnQoRulyC0OQWdofCC7finyyiIBlRSndQQ1gVDVv3jxEdmpqqrwP6cDEIZ3S0tI8PDx8fX2xP8n7GlVsMTExGNTMnTsXc095v9GLiWPA8ePHu3btKpdWJPn5+RjsYN8KDQ3lBX0rOEyiMeGdOnXq9u3b9Z+v0YWJYwBeYkdHR7m0Qrpw4cKCBQvc3d1XrlyZmZkp74xUfsXFxWGWPXv2bBx1bty4Ie8ZxcHEMQATig4dOsilFRtm78HBwd7e3n5+fiX7ZhaZv+zs7M2bN8/UEN8mlfeDEmHiGJCent62bVu5lDRu3769adMmT09PpE9QUJDlvuNDQlZW1tatW93c3FxcXObPn5+QkPDgwQP5v24cJo4BmD60bNlSLqVCrl69ivTBJH/OnDn+/v579+4t60s9Uak4efLkunXrkDKurq5z587dt2/f77//Lv93Sw8TxwB0mxYtWsilpNfNmzd/+eUXX19fd3d3TP6XLFly8ODB4n6shspIcnLy+vXrcWDAv8bDw2P16tWnT59++PCh/F8sG0wcA65fv25jYyOXUnEgtaOionx8fLB/Y0fHOAh7/NGjR1V+uJmMce7cObz4GHW6aeBfsHLlSoxr7t+/L/+fTIKJQ6ZWUFCAPX7jxo2enp4YBCGD0BOCg4NjYmL4o6DGuHjxYmxsbGRkJMJ9jgbyRfyECw6c8r/hCWHikFnIyMjYu3dvSEgIYshDw13zm4LoPygv8stZFRZyGcmyYcMGTFcRK+4aeMVwc+fOnZg0mebHzkqGiUPmK09z0gFjn/DwcMzFtMMIcPRet27dnj17MGIqwZchzBaGKkjYn3/+GZkSGhoqnqzIFE8N5PKuXbuOHz9+9epV+SUze0wcslSZmZmnT5/GCOjLL79s1qwZjvaiQ4rJmhJMgC6KrovjP+Jp//79SDF0adxX/4UdjJednX1aA4944MABhAi2IVRDO0cU2HIE69KlSyMiItD+xIkTGPqV+vvTTxYThywYJhcdOnT4/PPP9X9rGaMGNEhISBDdPuKxgIAAb29vLy1KZnlqxlPaN4tUuI2yKqzZ399feazdu3fj0ePj4zM0TPbekLlh4pBFys3N/fbbb1u2bBkVFSXXkRlj4pCFwegAsyQ7O7tZs2bl5+fL1WTemDiGNWrUqEw/hUnqnThx4uOPP+7Ro0dSUpJcR5aAiWNY06ZNb9++LZeSad26dWv69On29vbKT7mTJWLiGPb666/nldIXZ6lkNm7c6ODgMG7cOCMvlUBPHBPHMBsbG/P5yGZFc/bs2b59+zo6Oh46dEiuIwvExDEMI3lL/KiVpfvjjz88PT0R90uXLn1SXwKiUsfEMQzj+eJezJWMtGfPnrZt244YMYKvfDnDxDGsZcuWly5dkkupbGRlZQ0bNuydd96JiYmR68jyMXHIXGDqFBAQgGmUj49PQUGBXE3lAhOHzMLBgwfff//9fv36paeny3VUjjBx6AnLzc0dM2bMW2+9tXXrVrmOyh0mDj0xDx48EN9XcHZ2vnPnjlxN5RETh56MxMTEjz766JNPPuH3FSoUJg6ZWl5e3pQpU5o3bx4RESHXUXnHxCGTWrNmTYsWLSZOnHjz5k25jioAJo5h3bt3T0hIkEupmFJSUnr27NmlS5ejR4/KdVRhMHEMQz+JjY2VS0m1/Px8FxcXW1vboKCgcnYNTSouJo5hn3322a+//iqXkjrbtm1r2bLlN998k5OTI9dRxcPEMczJyYmfuC+B9PT0/v37d+zYMS4uTq6jioqJY9jAgQN//vlnuZR0Kygo8Pb2trGx8ff359e+SRsTx7DBgwfv2LFDLiUdoqOj27VrN2zYsKysLLmOKjwmjmHoPNu2bZNLqRBEzPDhwxE3CB25jkiDiUOlQPnaNyZT/No36cHEIWPFxcV17Nixf//+/No3GcTEoZLLycn55ptvWrZsyVknqcTEoZJ48OBBYGCgra2tq6srf6aO1GPiULEdOXLkww8/7NWrV2pqqlxHpBcTh4rh5s2bEyZMcHBwWLdunVxHpAITh1R5+PBheHi4vb391KlT+XuBVGJMHMNcXV39/Pzk0ork1KlT3bt3//jjj0+cOCHXERUHE8cwd3f3efPmyaUVg/Jr3ytXrsQwR64mKiYmjmE+Pj6enp5yaQWwYcMGBweH77//nj+CTKWFiWOYr6+vm5ubXFqunTlz5rPPPuOvfVOpY+IYtmjRImdnZ7m0nLp79+6cOXNsbW2XLVv2559/ytVExmHiGIa+N336dLm0PNqxY0fr1q1Hjx6dnZ0t1xGVBiaOYcHBwZMnT5ZLy5cLFy4MGjSoQ4cOBw4ckOuISg8Tp6K7d+/evHnzbGxsFixYgGW5mqhUMXEqtJiYmHfeeWfIkCGZmZlyHVEZYOJUUJcvXx4xYsTbb7/NC6qSKTFxKpz79+8vXrwY0yhPT88//vhDriYqS0yciiU+Pv6DDz5wcnI6d+6cXEdU9pg4FcW1a9fGjBnz1ltvbdmyRa4jMhUmTvn34MGDkJAQOzs7Z2fnO3fuyNVEJsTEMSwhIaFHjx5yqYVITEz86KOPPvnkk6SkJLmOyOSYOIYdPXq0a9eucqnZy8vLmzJlSvPmzSMiIuQ6oieEiWPYqVOnHB0d5VLztmbNmhYtWkyaNIlXzyKzwsQxLC0trX379nKpuUpJSenZsycGZceOHZPriJ40Jo5hFy5cePvtt7VL7t69q33TTOTn57u4uNjZ2QUHBz948ECuJjIDTJwi3L59WztTLl++7ODgoNyMjY3dvn27ctNMYJNatmz5zTff5OTkyHVEZoOJU7SZM2dieiKWc3NzbW1txTLiplmzZrdu3fpv0yft/PnzAwYM6NixY1xcnFxHZGaYOEXDTAop4+7url2IuGncuPFHH32kXfgE3bt3b+7cuTY2Nv7+/vzaN1kEJo5OCxcutLKy6tGjh5hhIW7s7OxeffVVHx8fuemTsHfv3nfeeWfo0KGXLl2S64jMFRNHp/v377///vv16tVr3rx5WFgY4qZv377W1tZPfEp1+fLlkSNH8mvfZImYOPocPny4adOmGOlgMjVixAgsdO7cWW5U2jBmQdjJpRp//vnn0qVLMY3y8PDg177JEjFxDBg7dmz9+vWtNLAQHh4utyhVDx48CAgIkEs1EH+Ojo59+vQ5e/asXEdkIZg4BuTl5TVp0kQkToMGDcp6SoUhzNq1a6XCGzdujBs3zsHBYcOGDVIVkWVh4hgWGhpar169unXr9u7dW64rVefOnSv81vvq1avt7e2nTZtW1mFHZAJMHFVatWr1j3/8o/DooxRhPtWpU6fPP/9cKUlKSurRo0e3bt34a99UbjBxVImPj8fcqkxHGfPnz8fETYRafn7+rFmz7OzsQkJC+H0FKk+YOP91//795OTkPXv2hIWF+fj4uGvMeWzMmDHKMqAKbdBy9+7dp0+fNvIDeJhPWVtbYxiFUIuKihLfV8jNzZXbEVm4Cp04165dQ/f29vZGgri5uSFEVq9evXfvXiQIenueIbg7Wu7bty8yMtLT03P27NliJVu2bCnWj7FgFNO+fXsMcLp27YpZVYcOHWJjY+VGROVChUucO3fubNu2DdHg6uq6cOFC9G014aLejRs3MAVbvny5i4sLMig8PPzKlSvyRvwdIg+jm3r16tnY2Pj6+ho5XCIyZxUlcfLz89H5Z82ahe69f/9+OSfKTGJiYkBAgLOzs7+//8WLF+XN0synxOd96tata29v379/fw8Pj82bNxs/UyMyQ+U/cRISEhA0mPUcPXpUzgMTSk9PX7x4MbYkLCxM+bgw5lPt2rUTH/ZRWFtbi4UGDRqMHj36l19+YfRQuVFuEwedef369VOnTl27di1mOnIAPDmHDh3ChA5DLcy23N3dkS9vvvlm69atbW1tMdh5++23R4wYgUkZBkcFBQXysyKycOUzcaKioqZMmWLK2VNxYYaFNGzYsGHHjh3HjRsXGBjIiKGKoLwlTlJSErJmx44dchc3P+fPnz9z5gyGOQEBAZw3UQVRfhIHnXbBggV+fn7Xr1+XO7d5w+hm0qRJBw8elJ8SUblTThIHkxTMTY4fPy735rJx48YNRNvp06flCiMEBQX5+Pj8+eef8nMjKkfKQ+LExMTMnDkzJydH7sR6RUVF2dvbb926VSkZNmwYRklaTXQ6dOhQpUqV4uPj5QrjHDlyZMKECQY/v0NkuSw+cTZt2rRkyRK576owY8YMpEanTp2UkldeeWXVqlVaTXRaunRpjRo1yuItsOzs7MmTJ6elpcnPk6hcsOzEWbNmDSYjcq9Vp3v37s8++yxCJzY2FjeTkpKwrEyUED0jR44cPXr0rl27RMnNmzcRNIMHD549e/aIESPatm2rlC9fvhzl06ZNu3r1qig0xvXr17Eqfl+cyiULTpw9e/agq8v9VTcfH59Zs2bt37//448/xs369evPmzevZs2a/fv3x83w8PDatWuLll9++eXLL788ZsyYjh07VqtW7fDhwyj84osvMAgaO3asra0tsmnUqFGiMcrt7OwwMKlTp467uztKAgMDJ02ahAhzdHQs7lxPQIohdC5cuCA/ZyILZ6mJc+bMGWdnZ7mn6jZlypQ2bdpEREQgaGJiYs6dO4fUOHr0KEYxTz/9dHJyMjKic+fOaJmQkICUEQMf9PwqVapgUIPQqVq16qFDh/I0X+B85plnFi9ejGU0w/QK4YKWffr0GT9+fJ5muITREwZH7733HhJKezPUy83NFWuTnzmRJbPIxHnw4AF6I3q+3E11GDduHAYmkZGRGNFER0ejZP369QgFxMTJkycRJciFLl26iB7es2dPJIW4440bNxAuLi4uTk5OSqE4bXzw4EEs//vf/66k5auvvhJtsKrXXnsN0YbAEuFVAhkZGdOnT5efPJEls8jECQoKEh1epVWrVr344osYvGCutHLlyjzNaeMOHTqI2l69ej333HPPP/+8OG2MbBo4cKCowlSrevXqKSkprVu3Hj58uCjUPm3cokULMSnTFhcXZ21tjRkfJmVDhw415gTzhg0boqKi5OdPZLEsL3Fu3bo1c+ZMuWsaMmDAgCFDhoSFhWFWlZ2d3b1792+//VZUYSQiRijitHGnTp1atmyZlpaGsUnz5s0xPkJht27dWrVqdeTIkcDAwFq1aimnjT/55JMGDRqgJYZLmHlh2IVca9y4MZLi/fffnzhx4uPHL7mpU6fyE8lUblhe4mCIcerUKblfGpKZmblv3z4sbNu2DX8RE9pvcrVv3145bYwAsrKyqqbRr1+/S5cuodDf3x+TL0yRMPlCDCmnjZFBzZo1Q1ohhhA0SJz09HQRQOKBjBcfH4/5oPwqEFkmy0uc6dOny52ytCEvkpOTpbeZsrKyMjIytEsUqamply9flktLD+aA8qtAZJksLHHQt4ODg+UeWd4tW7aMPy5O5YOFJU5gYGBKSorcI8u7kydPrlq1Sn4tiCyQhSXOnDlz5O5YMXh6esqvBZEFYuKUspycnISEhPj4+CtXrsh1RvDw8JBfCyILxMQpTc7OzlWrVhXvtdepUyc8PFxuUVJMHCofLCxxxBeXjLdo0SLxQZvStXLlygULFqSmpkZFRVlZWdnY2MgtSoqJQ+VDRUmcmzdvat/s3r37G2+8oV2Sp2kjNdOmp6pIgwcPxkgnOztbrigRnseh8sHCEickJCQ5OVnujnrFxsb26NGjRo0aL7zwgvgsz6RJk2rWrPn00083a9asb9++eZqr0gwdOhQNnnvuudGjRyvXLf3iiy/69OmDYUuTJk2whk8//VR8IFANBweHevXqyaUlcvLkSUzQ5NeCyAJZWOKkp6cX94I4bdq0QbKgx/r7+69evRolu3btat68ed26dVGyZs0alAwaNAhxgwHUxIkTq1SpMm/ePHHfjh07Vq9eHcHx/fffv/feexizTJ48WXvlhR07dmzx4sUIslq1akVGRsrVJbJs2bLLly/LrwWRBbKwxIEZM2bIPVKvV1555d1335XeOdKeVaWmpiJlvv76a3HT1ta2ffv2YhmJY2VlpVymq0WLFq1btxbLumA8Jc4cf/jhh+fPn5erS4SfOaZyw/ISJzQ0FOMIuVPqNm3aNPR/jFM8PDyUr3FrJ87WrVvRAMliq4EJV8OGDUUVEgdDJLEMmHlVrVr14sWLSkmRMO9bsWLFyy+/bG1tbfxVAX/99dfNmzfLrwKRZbK8xPn999+LO8z56aef7O3tESu9e/cWJdqJg/4sqhY8hlmMqJISZ9iwYWip69tVkh9++AGNN27cKFcUE9bDH3igcsPyEgc2bNiwc+dOuWvqhdFNnz59MEIRMx0kjjKQSUlJqVy5cpcuXf52Bw0pcRwcHF566SWten28vb2ROAg7uaI4MKCLj4+Xnz+RxbLIxIGpU6dmZmbKHbSQmzdvDh8+PCIiIjEx0dHRETMd8SXvH3/8EXGwdu3arKws3Ozfv7+4EmBcXNzu3bvFFUXzNInz1FNPofHx48cx1qhWrZq4TqAuYWFhmzZtwkNs2bKlSZMm1atXT0pKkhupdurUKcSW/MyJLJmlJg6iZOLEiQYvPIpm//znP6tUqYJ8sbW1RSKI8pMnTzZv3hyFyJQ8zZUoBg0ahEBBCSLmgw8+EM1Q26BBgzfffBPlGAd9+umn+r+74OTkJE4bA9INIxS5hWoXLlyYMmXK/fv35WdOZMksNXHg7NmzGH2ouaYnYqLIT/FgAKKdIDk5OceOHdO+LI4yq8LdDZ4wFrBV0dHRmAqV7FccBDzWhAkT7ty5Iz9nIgtnwYkDiAxMrwyOdEpMOo8jZGRk2BVF5Rllg2E56XUAAA4xSURBVFJSUsTlSuVnS2T5LDtx4Pz585MmTSqt3i5Bzx8zZoxUiMHLsqIYM6hR7N27FwO3goIC+XkSlQsWnziPNNdanzZtGvqq3H0tyvXr1xcsWBAaGio/PaJypDwkjoC+6uXlVVrfnDSx2NjYyZMnJycny8+KqHwpP4kDly9fxpRk/fr1coc2Y2fPnp01a1Z4ePjDhw/l50NU7pSrxBEOHDgwderULVu2yJ3bzKSmprq5uWEmlceTxFRhlMPEEfbu3Ttz5swVK1aY4TwrOjoa27Zo0aLbt2/L201UrpXbxBHOnDnj4eGBoYT4ufEn6/Tp0xjRODs779ixg1+VooqpnCeOcO/evV27ds2ePRvRExUVZcpRz40bN3799VcfH59Zs2aFhITk5ubKG0dUkVSIxFEgeg4cODB37lykj5eX16ZNmy5cuCCHhNEyMzN37tw5b948FxcXd3d3ZNytW7fkTSGqkCpW4mj7/fffDx8+vHTpUjcNZNDChQs3b9585MiR1NRUOUV0SEtLO3r06JYtWwICAlxdXcUwCssY1+TxfDBRIRU3cQq7fv36b7/9tn79+uDgYIxN5mgRqSRolwcFBa1duzY+Pv7q1at8e5vIICaOWlZWVnIRERUTE0ctJg6R8Zg4ajFxiIzHxFGLiUNkPCaOWkwcIuMxcdRi4hAZj4mjFhOHyHhMHLWYOETGY+KoxcQhMh4TRy0mDpHxmDhqMXGIjMfEUYuJQ2Q8Jo5aTBwi4zFx1GLiEBmPiaMWE4fIeEwctZg4RMZj4qjFxCEyHhNHLSYOkfGYOGoxcYiMx8RRi4lDZDwmjlpMHCLjMXHUYuIQGY+JoxYTh8h4TBy1mDhExmPiqMXEITIeE0ctJg6R8Zg4ajFxiIzHxFGLiUNkPCaOWkwcIuMxcdRi4hAZj4mjFhOHyHhMHLWYOETGY+KoxcQhMh4TRy0mDpHxmDhqMXGIjMfEUYuJQ2Q8Jo5aTBwi4zFx1GLiEBmPiaMWE4fIeEwctZg4RMZj4qjFxCEyHhNHLSYOkfGYOGoxcYiMx8RRi4lDZDwmjlpMHCLjMXHUYuIQGY+JoxYTh8h4TBy1mDhExmPiqMXEITIeE0ctJg6R8Zg4ajFxiIzHxFGLiUNkPCaOWkwcIuMxcdRi4hAZj4mjFhOHyHhMHLWYOETGY+KoxcQhMh4TRy0mDpHxmDhqMXGIjMfEUYuJQ2Q8Jo5aTBwi4zFx1GLiEBmPiaMWE4fIeEwctZg4RMZj4qjFxCEyHhNHLSYOkfGYOGoxcYiMx8RRi4lDZDwmjlpMHCLjMXF0SktL074pJY5US0RqMHF0Wrx4cWxsrHJTO3FQjlrlJhGpxMTRKTc318bGRgkdJXFQgnLU/rcpEanDxNFn7NixDRs2FKEjEgfLjRs3dnJykpsSkQpMHH1SU1Pr1q376quvImiQOPj7+uuvYyE6OlpuSkQqMHEM6NOnzz/+8Y+mTZsiaGxtbR0cHOzt7eVGRKQOE8eAvXv32tjYYKTTpEmTjz76CLmzZMkSuRERqcPEMezdd99F4lhpNGrUSK4mItWYOIaFh4fb2dmJxJkwYYJcTUSqMXEMKygowMQKcWNtbZ2fny9XE5FqTBxV5s6di4kV3xQnMhITR5Xc3NwmTZqkp6fLFURUHEwcA9LS0hYuXOjt7f3dd995eXlhmd+oIioxJo5OWVlZLi4uoaGhly9fznvs0qVLwcHBs2bNQq18ByIyhIlTtGPHjrm6ul6/fl3JGm05OTnOzs5oI9+NiPRi4hQB4xfEza1bt+Sk+buZM2dypENULEycImDSdO3aNTlgCsFI58cff5TvTES6MXFkqampK1eulNNFh+Dg4JSUFHkVRKQDE0cWEBCgfapYv8zMTD8/P3kVRKQDE0fm7e0t54pec+bMkVdBRDowcWReXl5yqOjl5uYmr4KIdGDiyJg4RGWHiSMLCAi4dOmSnCs6ZGZm+vr6yqsgIh2YOLLU1NSQkBA5WnQICgo6ffq0vAoi0oGJU4SZM2fm5OTI6VII2kybNk2+MxHpxsQpQlZWlrOzsxwwhcyYMSMjI0O+MxHpxsQp2rFjx/SMdFCOuDl06JB8NyLSi4mjE0Y6P/7444oVKzIzM5WswTJKpk+fztENUQkwcQxISUmZP3/+nDlz3DTmzZuXlJQkNyIidZg4RGQ6TBwiMh0mDhGZDhOHiEyHiUNEpsPEISLTYeIQkekwcYjIdJg4RGQ6TBwiMh0mDhGZDhOHiEyHiVO04ODgH374QS4lIuMwcf7y8OFD7Zs9e/a0tbXVLhGkZhL9tUTExHmUmJj46aef1qxZ88UXX3R1dUXJjBkzatWq9fTTT9vY2AwcOBAlBQUFEyZMqF+/PgrfeuutXbt2KXcfPnz4gAEDli1b9tprr2ElTk5Ot27dUmqJSBsT51G7du2QLJs2bQoMDNy8eTNKDhw48Oabb9arVw8l27ZtQ8mXX35ZrVq1L774Yvny5Q4ODpUrV46Pjxd3d3R0rFGjhrW19ZQpUzp16lSpUiX+GDmRLkycR1ZWVu+9915+fr52ofas6sqVK1WqVME4SNy8du0aEgcNxE0kTt26dZVLAmIE1LZtW7FMRBImziMXFxcMTDBI8fX1ffDggSjUTpyYmBg0mD9/vnIXVL3++utiGYmDUZJSNWrUqKpVq968eVMpISIFE+f/RUZGYq6EWOnXr58o0U6cnTt3omrZsmVK+1atWr366qtiWUqcr776Co1v3LihlBCRgonzF4xuBgwYgOEJJk242atXr0aNGomq9PR0hMjgwYPFzTt37lSrVu1f//qXuCklTsuWLWvXrq3cJCJtFT1xHj58OHr06C1btpw9e7ZLly516tRBoKDczc0NKbN9+/bbt2/jZrdu3Z5//nk/P7/Dhw/37dsXVRs2bBBrQOI89dRTaH/u3DlnZ2eE0dSpU7UfgogUTJyHb7zxRpUqVRAizZs337hxoyg/f/78m2++iUIEyiPNyeOuXbtWrlwZJbVq1Zo7d66yBjTADAvzLFShgZOT0927d5VaItJW0RNHyM/Pv3Tpklz66NHFixe138O6desWkkg5uywosyqsgSeMifRj4hhLOo9DRHowcYw1ffr0CRMmyKVEVBQmDhGZDhOHiEyHiUNEpsPEISLTYeIQkekwcYjIdJg4RGQ6TBwiMh0mDhGZDhOHiEyHiUNEpsPEISLTYeIQkekwcYjIdJg4RGQ6TBwiMh0mDhGZDhOnAjl37pz4bRzh/v37x44d06rXp1iNy8IT3wAqFUycCmTt2rWJiYnKzTt37nh6emrV61OsxmVBzwZs27YtIiJCLM+bN48/T2jOmDgVSLlMnDNnzvj4+CiJc/36denHNsisMHGegPz8/MWLF//666/z58/funWr8gM1hw8f9vPzQ+Evv/yCm8HBwWIStGHDhtjYWCwkJyfv3bv34MGDaIM+lpubq6wtJiYG98VNqRbi4uJQsm7dusDAwMKJo70Zq1atOnv27CPNL5SuWLFC/FigdmMs/PHHH5GRkbjLTz/9hO4tHj0hIcHX1xcPKno7thYNEHAoOX36tFiDVKi92dI6HxX1EokNkB4Id1y0aNGRI0eUxMFm5+XliWUyQ0ycJwCdx93dHZ0tJycnJCQkPj4ehb///vvcuXMzMzORFAEBAVeuXEHQoC8VFBR4eXmFhoaizaZNm06cOLFgwYLbt28jR3bs2KGsbefOnbdu3bp7965UK1Z7+fLlS5cuoQ9LiSNtBpbxEKhKTU1F+igtRWOROPfv309PT//zzz83btyIEBErwaaKzT5//jzSwdvbGw+XlZWlPGLhQu3NltZZ5LYVfqBHmvkU2uNF46zKUjBxngDtCQIO2ji8Y+HUqVMiVh5pOhIO70ePHkUHS0tL27JlC1IDx3MMB1C4cOHCqKioNWvWLFu2TKzNw8Pj4cOHWEYeSbUYFq1cuVKsVs+sSmzG1atXMUO5d+8eBkTHjx9XWkqNER8pKSlhYWG7du0Sj46wQPnq1avxLLANhR+xcKH2ZkvrfFTUthV+IAzH/P39Ec1YOV46MbRh4pg5Js4TIHUn8dPDiJLw8HBRiCM/JlaYX2BOgWXRFTGfQsdDX0WUpGqIGZD22grXYnQgEu2RocQRm4FZCSZ3eFzkjtJSuzHGKahFG0x2ROIoK8FAA0GAcRm2VpQoj1i4UPuO0jofFbVthR/o0KFDQRoYNCGR9+zZ84iJY/aYOE+AOFxjpvPgwQMMRpA1KMRUCL0FVRjLIDUuXryIQkwfxOhm//79mFuh32KWhOmJOEeDyYhYm9IVC9diFuPr64uZC8YvmHBJiVN4Mw4ePIgHwiBLaaY0Fo+CwRdGXhhrbN68GYOpwkGADo+BUlJSEhIEVeIRCxdq31Fap3g4adsKP5BYfqQ5ecxZlaVg4jwBdzSnJNavX48ZEMYUd+/eFeW7d+9GvixatAiHazHd2L59uxgaZGRk4C5i4hAbG4tQWLp0aWBgoFib0hUL12I9GDigtyPFQkJCpMRBubQZeAg8EB5OaaY0Fo+SnZ3tq4F0QPdGby8cBCdPnvzpp5+w8atWrVKiQSrU3mxpnRjyFH6JVCYOXkAmjjlj4jwBSufB4EWqwlxGnKrQD20wnJFLHytcW1BQoH1TIcq1NwPDCkxS/tuiKBh3iAFU4e0XMF7DXwysMODC2EpPoUJap56XSBesAU8HGSrWQ+aJifME5Ofni3eyzdCuXbvi4uLk0uJA3i1ZsgQjteDgYMyh9BTqUYKXaO3atVj/vn375AoyJ0wcKhPKm1AGC0tRWa+fjMfEISLTYeIQkekwcYjIdJg4RGQ6TBwiMh0mDhGZzv8BIHDM7XoieToAAAAASUVORK5C"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var representation = compiledWorkflow.getGraph( GraphRepresentation.Type.PLANTUML, \"merged sub graph\", false );\n",
    "\n",
    "displayDiagram( representation );"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Java (rjk 2.2.0)",
   "language": "java",
   "name": "rapaio-jupyter-kernel"
  },
  "language_info": {
   "codemirror_mode": "java",
   "file_extension": ".jshell",
   "mimetype": "text/x-java-source",
   "name": "java",
   "nbconvert_exporter": "script",
   "pygments_lexer": "java",
   "version": "17.0.2+8-86"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
